| 1. |
- Catelani, Marcantonio, et al.
(författare)
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A Practical Solution for HVAC Life Estimation Using Failure Models
- 2020
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Ingår i: 17th IMEKO TC 10 and EUROLAB Virtual Conference. - : International Measurement Confederation (IMEKO). ; , s. 85-91
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Konferensbidrag (refereegranskat)abstract
- Heating, ventilation, and air conditioning (HVAC) is the technology of indoor and vehicular environmental comfort. The objectives of HVAC systems are to provide an acceptable level of occupancy comfort and process function, to maintain good indoor air quality, and to keep system costs and energy requirements to a minimum. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Reliability under a range of conditions is one of the most important requirements to guarantee in HVAC installed on trains. Predicting the reliability of mechanical equipment requires the consideration of its exposure to the environment and subjection to a wide range of stress levels such as impact loading. Often analysist find an unavailability of failure data in handbooks and problems for acquiring data for mechanical components, so the mentioned problems demonstrates the need for reliability prediction models. The paper deals with a HVAC installed on a high-speed train and evaluates the failure rates through the failure rate models suggested by the handbooks in order to assess a model which includes all the mechanical parts.
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| 2. |
- Catelani, Marcantonio, et al.
(författare)
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Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models
- 2021
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Ingår i: Acta IMEKO. - : International Measurement Confederation (IMEKO). - 0237-028X. ; 10:3, s. 100-107
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Tidskriftsartikel (refereegranskat)abstract
- Heating, ventilation, and air conditioning (HVAC) is a widely used system used to guarantee an acceptable level of occupancy comfort, to maintain good indoor air quality, and to minimize system costs and energy requirements. If failure data coming from company database are not available, then a reliability prediction based on failure rate model and handbook data must be carried out. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Otherwise, if field data regarding the component failures are available, then classical reliability assessment techniques such as Fault Tree Analysis and Reliability Block Diagram should be carried out. Reliability prediction of mechanical components is a challenging task that must be carefully assessed during the design of a system. For these reasons, this paper deals with the reliability assessment of an HVAC using both failure rate model for mechanical components and field data. The reliability obtained using the field data is compared to the one achieved using the failure rate models in order to assess a model which includes all the mechanical parts. The study highlights how it is fundamental to analyze the reliability of complex system integrating both field data and mathematical model.
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| 3. |
- Catelani, Marcantonio, et al.
(författare)
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FMECA assessment for railway safety-critical systems investigating a new risk threshold method
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 86243-86253
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Tidskriftsartikel (refereegranskat)abstract
- This paper develops a Failure Mode, Effects and Criticality Analysis (FMECA) for a heating, ventilation and air conditioning (HVAC) system in railway. HVAC is a safety critical system which must ensure emergency ventilation in case of fire and in case of loss of primary ventilation functions. A study of the HVAC’s critical areas is mandatory to optimize its reliability and availability and consequently to guarantee a low operation and maintenance cost. The first part of the paper describes the FMECA which is performed and reported to highlight the main criticalities of the HVAC system under analysis. Secondly, the paper deals with the problem of the evaluation of a threshold risk value, which can distinguish negligible and critical failure modes. Literature barely considers the problem of an objective risk threshold estimation. Therefore, a new analytical method based on finite difference is introduced to find a univocal risk threshold value. The method is then tested on two Risk Priority Number datasets related to the same HVAC. The threshold obtained in both cases is a good tradeoff between the risk mitigation and the cost investment for the corrective actions required to mitigate the risk level. Finally, the threshold obtained with the proposed method is compared with the methods available in literature. The comparison shows that the proposed finite difference method is a well-structured technique, with a low computational cost. Furthermore, the proposed approach provides results in line with the literature, but it completely deletes the problem of subjectivity.
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| 4. |
- Catelani, Marcantonio, et al.
(författare)
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Optimizing Maintenance Policies for a Yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring
- 2020
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 69:9, s. 6241-6249
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Tidskriftsartikel (refereegranskat)abstract
- System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM.
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| 5. |
- Catelani, Marcantonio, et al.
(författare)
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Risk Assessment of a Wind Turbine : A New FMECA-Based tool with RPN threshold estimation
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 20181-20190
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Tidskriftsartikel (refereegranskat)abstract
- A wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW on-shore wind turbine.
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| 6. |
- Ciani, L., et al.
(författare)
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Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection
- 2021
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 10:12
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Tidskriftsartikel (refereegranskat)abstract
- Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.
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| 7. |
- Ciani, Lorenzo, et al.
(författare)
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Improving Human Reliability Analysis for railway systems using fuzzy logic
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 128648-128662
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Tidskriftsartikel (refereegranskat)abstract
- The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert’s subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, α-cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach.
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| 8. |
- Ciani, Lorenzo, et al.
(författare)
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Reliability evaluation of an HVAC ventilation system with FTA and RBD analysis
- 2020
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Ingår i: 2020 International Symposium on Systems Engineering (ISSE) Proceedings. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Rail industry is rapidly developing, and rail becomes ever more viable in a wide range of regions. Therefore, the passenger experience and comfort has become a major concern for operators in the world. Heating, ventilation and air conditioning systems are used in railways to provide passengers thermal comfort and proper air motion. The ventilation system is one of the main elements of the system. Its components include both mechanical and electronic devices. All components are subjected to stress, and this tends to reduce their life cycle; the reliability of the ventilation system must be evaluated to plan and schedule appropriate maintenance activities. The paper evaluates reliability of the ventilation system using fault tree analysis and a reliability block diagram. Both techniques analyse data qualitatively; moreover, with specific algorithms they also provide quantitative results in term of reliability and probability of system failure. The paper compares the two reliability evaluation methods to verify their accuracy.
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| 9. |
- Diez-Olivan, Alberto, et al.
(författare)
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Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis Under Changing Conditions
- 2021
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE. - 1551-3203 .- 1941-0050. ; 17:11, s. 7760-7770
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Tidskriftsartikel (refereegranskat)abstract
- Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.
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| 10. |
- Galar, Diego, et al.
(författare)
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Advanced Analytics for Modern Mining
- 2022
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Ingår i: Advanced Analytics in Mining Engineering. - Cham : Springer Nature. ; , s. 23-54
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 11. |
- Galar, Diego, et al.
(författare)
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Big Data in Railway O&M: A Dependability Approach
- 2022
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Ingår i: Research Anthology on Big Data Analytics, Architectures, and Applications. - : IGI Global. ; , s. 391-416
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Railway systems are complex with respect to technology and operations with the involvement of a wide range of human actors, organizations and technical solutions. For the operations and control of such complexity, a viable solution is to apply intelligent computerized systems, for instance, computerized traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by only applying operation and maintenance activities. Indeed, safety is a more difficult goal to achieve using traditional maintenance strategies and computerized solutions come into the picture as the only option to deal with complex systems interacting among them and trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Big data analytics are expected to improve the overall performance of the railways supported by smart systems and Internetbased solutions. Operation and Maintenance will be application areas, where benefits will be visible as a consequence of big data policies due to diagnosis and prognosis capabilities provided to the whole network of processes. This chapter shows the possibilities of applying the big data concept in the railway transportation industry and the positive effects on technology and operations from a systems perspective. © 2022 by IGI Global. All rights reserved.
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| 12. |
- Galar, Diego, et al.
(författare)
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Robotics and artificial intelligence (AI) for maintenance
- 2023
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Ingår i: Monitoring and Protection of Critical Infrastructure by Unmanned Systems. - : IOS Press. - 9781643683768 - 9781643683775 ; , s. 206-223
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This paper reviews the application of AI in maintenance and inspections. It gives an overview of the development of AVs and distant inspection operations for industrial assets using unmanned aerial vehicles (UAVs). It discusses the use of AVs in infrastructure inspection and explain the types of sensors used for these applications. It explains how autonomous robots, including drones, are currently used in various industrial settings for inspection and maintenance. The paper concludes by discussing the use of AI in predictive maintenance.
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| 13. |
- Galvez, Antonio, et al.
(författare)
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A Hybrid Model-Based Approach on Prognostics for Railway HVAC
- 2022
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 108117-108127
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Tidskriftsartikel (refereegranskat)abstract
- Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.
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| 14. |
- Galvez, Antonio, et al.
(författare)
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Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
- 2021
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Ingår i: International Journal of Hydromechatronics. - : InderScience Publishers. - 2515-0464 .- 2515-0472. ; 4:3
-
Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a physics-based model which is part of a hybrid model (HyM). The physics-based model is developed for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage. This model will be used to generate data for building a data-driven mode. Thus, the combination of these two models provides the hybrid model-based approach (HyMAs). The physics-based model of the HVAC system is divided into four principal parts: cooling subsystems, heating subsystems, ventilation subsystems, and vehicle thermal networking. First, the subsystems are modelled, considering the sensors embedded in the real system. Next, the model is synchronised with the real system to give better simulation results and validate the model. The cooling subsystem, heating subsystem and ventilation subsystem are validated with the acceptable sum square error (SSE) results. Second, the new virtual sensors are defined in the model, and their value to future research is suggested
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| 15. |
- Galvez, Antonio, et al.
(författare)
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Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
- 2021
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.
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| 16. |
- Gálvez, Antonio, et al.
(författare)
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Feature Assessment for a Hybrid Model
- 2023
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Ingår i: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021. - : Springer Nature. ; , s. 43-58
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Konferensbidrag (refereegranskat)abstract
- This paper proposes an assessment of features orientated to improve the accuracy of a hybrid model (HyM) used for detecting faults in a heating, ventilation, and air conditioning (HVAC) system. The HyM combines data collected by sensors embedded in the system with data generated by a physics-based model of the HVAC. The physics-based model includes sensors embedded in the real system and virtual sensors to represent the behaviour of the system when a failure mode (FM) is simulated. This fusion leads to improved maintenance actions to reduce the number of failures and predict the behaviour of the system. HyM can lead to improved fault detection and diagnostics (FDD) processes of critical systems, but multiple fault detection models are sometimes inaccurate. The paper assesses features extracted from synthetic signals. The results of the assessment are used to improve the accuracy of a multiple fault detection model developed in previous research. The assessment of features comprises the following: (1) generation of run-to-failure data using the physics-based model of the HVAC system; the FMs simulated in this paper are dust in the air filter, degradation of the CO2 sensor, degradation of the evaporator fan, and variations in the compression rate of the cooling system; (2) identification of the individual features that strongly distinguish the FM; (3) analysis of how the features selected vary when components degrade.
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| 17. |
- Galvez, Antonio
(författare)
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Hybrid digital twins: A co-creation of data science and physics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Safety is more important than reliability or efficiency in railway, aerospace, oil & gas, and chemical industries. Regulations are very restrictive in sectors where safety is paramount. This makes maintainers replace critical components in initial stages of degradation, which implies a loss of useful life and a lack of information about advanced stages of degradation for those components. Nevertheless, this lack of data can be overcome using hybrid digital twins, also known as hybrid-model based approaches (HyMAs), which combine data-driven models with physics-based models. This fusion minimizes the occurrence of undesirable failures that may interrupt the functionality of critical systems in a safe or cost-efficient manner.HyMAs have been studied at Luleå University of Technology by other Ph.D. students who found promising direction for future research in prognostics and health management (PHM) applications. Thus, this research work continues the direction defined in previous research with the proposal of HyMAs for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage orientated to diagnostics and prognostics processes. The proposed hybrid modelling consists of the fusion of data obtained from two sources: data obtained from the real system and synthetic data generated by a developed physics-based model of the HVAC.The HVAC system is considered a system of systems (SoS). Therefore, the physics-based model of the HVAC system is divided into four main systems: heating subsystem, cooling subsystem, ventilation subsystem, and cabin thermal networking subsystem. These subsystems are modelled considering the sensors installed in the real system and soft sensors, also known as virtual sensors, which provide crucial information for fault detection, diagnostics, and prognostics. These sensors defined in the physics-based model generate synthetic data which reproduce the behaviour of the system while a failure mode (FM) is simulated. Verification and validation are key processes to synchronise the response of the physics-based model with the signals obtained from the real system. Hence, the physics-based model is synchronised, verified, and validated using data collected by sensors located in the real system. These steps are conducted following guidelines suggested in the literature.Different datasets containing real data and synthetic data while the HVAC system works in faulty and healthy states are used to train data-driven models for fault detection and diagnostics and to train data-driven models for prognostics.Statistical features, such as shape factor, kurtosis, skewness, and sum square error, among others, are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data obtained from the real system. The data fusion is classified according to the condition indicators of the system in terms of FMs and level of degradation. The merged features are used to train data-driven models for fault detection and diagnostics. In addition, the real data can be loaded to the physics-based model to predict the degradation of the air filter.Then, the prediction data are loaded to an exponential model that provides an estimation of the remaining useful life (RUL) of the air filter. To improve the prognostics model, the physics-based model is used to generate run-to-failure data which are used to train and test a deep convolutional neural network (CNN) which accurately estimates the RUL of the air filter.
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| 18. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Model Development for HVAC System in Transportation
- 2021
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Ingår i: Technologies. - : MDPI. - 2227-7080. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models.
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| 19. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway
- 2021
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Ingår i: Simulation Notes Europe. - : ARGESIM Publisher. - 2306-0271 .- 2305-9974. ; 31:3, s. 121-126
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Tidskriftsartikel (refereegranskat)abstract
- Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway’s maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers.
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| 20. |
- Gálvez, Antonio, et al.
(författare)
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Synthetic Data Generation in Hybrid Modelling of Railway HVAC System
- 2020
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Ingår i: 17th IMEKO TC 10 and EUROLAB Virtual Conference. - : International Measurement Confederation (IMEKO). ; , s. 79-84
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a hybrid model (HyM)for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC.The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated.Statistical features are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy.Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.
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| 21. |
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| 22. |
- Juuso, Esko, et al.
(författare)
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Preface
- 2023
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Ingår i: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021. - : Springer. - 9789819919871 - 9789819919888 ; , s. v-vi
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 23. |
- Karim, Ramin, 1964-, et al.
(författare)
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AI Factory -- A Framework for Digital Asset Management
- 2021
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Ingår i: Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021). - Singapore : Research Publishing Services. ; , s. 1160-1167
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Konferensbidrag (refereegranskat)abstract
- Advanced analytics empowered by Artificial Intelligence (AI) contributes to the achievement of global sustainability and business goals. It will also contribute to global competitiveness of enterprises through enablement of fact-based decisionmaking and improved insight. The digitalisation process currently ongoing in industry, and the corresponding implementation of AI technologies, requires availability and accessibility of data and models. Data and models are considered as digital assets (ISO55K) that impact a system’s dependability during its whole lifecycle. Digitalisation and implementation of AI in complex technical systems such as found in railway, mining, and aerospace industries is challenging. From a digital asset management perspective, the main challenges can be related to source integration, content processing, and cybersecurity.However, to effectively and efficiently retain the required performance of a complex technical system during its lifecycle, there is a need of appropriate concepts, methodologies, and technologies. With this background, Luleå University of Technology, in cooperation with a number of Swedish railway stakeholders – fleet managers, railway undertakings, infrastructure managers and Original Equipment Manufacturers (OEM), has created a universal platform called ‘the AI Factory’ (AIF). The concept of AIF has further been specialised for railway industry, so called AI Factory for Railway (AIF/R).Hence, this paper aims to provide a description of findings from the development and implementation of ‘AI Factory (AIF)’ in the railway context. Furthermore, the paper provides a case-study description used to verify the developed technologies and methodologies within AIF/R.
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| 24. |
- Karim, Ramin, et al.
(författare)
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AI Factory: Theories, Applications and Case Studies
- 2023. - 1
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features:• Presents a compendium of methodologies and technologies in industrial AI and digitalisation.• Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.• Covers a broad range of academic and industrial issues within the field of asset management.• Discusses the impact of Industry 4.0 in other sectors.• Includes a dedicated chapter on real-time case studies.This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.
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| 25. |
- Kumar, Uday, et al.
(författare)
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Editorial
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 - 9783031396199 ; , s. v-vi
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 26. |
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| 27. |
- Murua, M., et al.
(författare)
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Solving the multi-objective Hamiltonian cycle problem using a Branch-and-Fix based algorithm
- 2022
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Ingår i: Journal of Computational Science. - : Elsevier. - 1877-7503 .- 1877-7511. ; 60
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Tidskriftsartikel (refereegranskat)abstract
- The Hamiltonian cycle problem consists of finding a cycle in a given graph that passes through every single vertex exactly once, or determining that this cannot be achieved. In this investigation, a graph is considered with an associated set of matrices. The entries of each of the matrix correspond to a different weight of an arc. A multi-objective Hamiltonian cycle problem is addressed here by computing a Pareto set of solutions that minimize the sum of the weights of the arcs for each objective. Our heuristic approach extends the Branch-and-Fix algorithm, an exact method that embeds the problem in a stochastic process. To measure the efficiency of the proposed algorithm, we compare it with a multi-objective genetic algorithm in graphs of a different number of vertices and density. The results show that the density of the graphs is critical when solving the problem. The multi-objective genetic algorithm performs better (quality of the Pareto sets) than the proposed approach in random graphs with high density; however, in these graphs it is easier to find Hamiltonian cycles, and they are closer to the multi-objective traveling salesman problem. The results reveal that, in a challenging benchmark of Hamiltonian graphs with low density, the proposed approach significantly outperforms the multi-objective genetic algorithm.
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| 28. |
- Murua, Maialen, et al.
(författare)
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Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing : Sequence Strategy Generation
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 91574-91585
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Tidskriftsartikel (refereegranskat)abstract
- The tool-path problem has been extensively studied in manufacturing technologies, as it has a considerable impact on production time. Additive manufacturing is one of these technologies; it takes time to fabricate parts, so the selection of optimal tool-paths is critical. This research analyzes the tool-path problem in the direct energy deposition technology; it introduces the main processes, and analyzes the characteristics of tool-path problem. It explains the approaches applied in the literature to solve the problem; as these are mainly geometric approximations, they are far from optimal. Based on this analysis, this paper introduces a mathematical framework for direct energy deposition and a novel problem called sequence strategy generation. Finally, it solves the problem using a benchmark for several different parts. The results reveal that the approach can be applied to parts with different characteristics, and the solution to the sequence strategy problem can be used to generate tool-paths.
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| 29. |
- Naim, Wadih
(författare)
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On the Role of Data Quality and Availability in Power System Asset Management
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability.The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality.Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling. While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy.
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| 30. |
- Pinto, Constâncio António, et al.
(författare)
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Stochastic versus Fuzzy Models-A Discussion Centered on the Reliability of an Electrical Power Supply System in a Large European Hospital
- 2022
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increase the system’s reliability based on the results achieved.
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| 31. |
-
Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021
- 2023
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Proceedings (redaktörskap) (refereegranskat)abstract
- This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.
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| 32. |
- Raposo, Hugo, et al.
(författare)
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An Integrated Model for Dimensioning the Reserve Fleet based on the Maintenance Policy
- 2021
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Ingår i: WSEAS transactions on systems and control. - : World Scientific and Engineering Academy and Society. - 1991-8763 .- 2224-2856. ; 16, s. 43-65
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Tidskriftsartikel (refereegranskat)abstract
- Usually, the Reserve Fleet, or Spare Fleet, of passenger urban buses, is based on indicators used in some international relevant companies and extrapolated for many others, almost as a dogma. However, it must be taken into consideration pragmatic variables intrinsic to the buses namely their maintenance and in a more pragmatic approach, indexing their availability and by consequence the reserve fleet indexed to the maintenance policy used in each company.The paper discusses these subjects and presents a global model that integrates the maintenance planning policy, based on a condition monitoring model, maintenance Key Maintenance Indicators (KPI), and an economic life cycle model.The paper presents some results based both in theoretical considerations and also in real data from an urban fleet of a European Country.
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| 33. |
- Shao, Haidong, et al.
(författare)
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A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
- 2021
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 74, s. 65-76
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Tidskriftsartikel (refereegranskat)abstract
- Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.
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| 34. |
- Teymourian, Kiumars, et al.
(författare)
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Ergonomics Evaluation in Designed Maintainability:Case Study Using 3 DSSPP
- 2021
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Ingår i: Management Systems in Production Engineering. - : Sciendo. - 2299-0461 .- 2450-5781. ; 29:4, s. 309-319
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Tidskriftsartikel (refereegranskat)abstract
- Maintainability is one of the design parameters (reliability, availability, maintainability, and safety (RAMS)) and maintenance is needed to keep the respective design in sustainable use. At the same time, the human is involved in the form of interface and interaction in an engineered product/system designed. Ergonomics is a multi-disciplinary science that considers human capabilities and limitations in a broader sense. The objective of this paper is to integrate ergonomics into the maintainability design process in order to facilitate maintenance operation in lesser; time, cost, easier operation as well as the well-being of human who is involved. In other words, good ergonomics lead to good economics and in a broader sense, sustainability. This investigation shows that designing comfortable workplaces and lesser workload for maintenance operators will be beneficial for the maintainability design process and also improve the meantime to repair MTTR. In order to evaluate the effect of designed work-place and workload on maintainers 3 D Static Strength Prediction Program (3D SSPP) that is commonly used as an ergonomics evaluation tool in scientific studies was applied.
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| 35. |
- Teymourian, Kiumars
(författare)
-
Integrating Ergonomics in Maintainability Design Process
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Engineered designed systems or products influence the intended humans through their interactions and interface with the systems. A system or product is an object whereas a human is a living complex system, and most of human characteristics, such as capabilities and limitations, are known. During the design process, from the conceptual phase to phasing out, maintainability engineers are involved in making the designed system/product easier for its maintaining. Maintainability is a process and it is one of the design parameters, which will affect maintenance that is required as a result of the design. Maintainability and maintenance are two innate factors in a system/product that influence the health of human users. Much literature and many reports focus on human maintenance operators who, due to their working conditions, confront with many; risks, incidents, and accidents and the consequences of these situations result in many serious injuries, illnesses, and even fatalities. These unanticipated events result from a lack of synchronization between the design of the tasks required for maintenance performance, human capabilities, and limitations. The argument of this thesis is that an active integration of cognizant ergonomics expertise, in the maintainability design process, will result in viable system/product functionality, cost savings, the well-being of involved humans, and organizational efficiencies. In study I, the simulation approach was used to identify the critical posture of the maintenance personnel, and to implement the defined postures with minimal loads on the personnel who used the equipment in a practical scenario. The simulation results were given to the designers to use to improve the workplace/equipment, in order to reduce maintenance time, which is a key parameter in maintainability. The study also described product design workflow, and the role of ergonomist participation in the design. In study II, two relevant tools, Hierarchical Task Analysis (HTA) and William Fine method, were applied in order to prevent serious accidents and make task performances safer for maintainers. The results presented a clearer understanding of the differences between “work-as-done” and “work-as-imagined”, for both manager and operators. Study III used an injuries survey, completed by maintenance operators, in a study of compression on their lower backs. This study reveals an absence of effective maintainability design during the product design stage. The general conclusion of these studies is that maintenance operators, due to the nature of their work, are exposed to more risks, and that ergonomics considerations, during the maintainability design, will lead to healthier working conditions. Different ergonomics tools were used and the results have shown how working conditions improved. These improvements were suboptimal concerning micro and macro-ergonomics aspects, due to the pre-existing working situations.
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| 36. |
- Thaduri, Adithya, et al.
(författare)
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Space weather climate impacts on railway infrastructure
- 2020
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Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer Nature. - 0975-6809 .- 0976-4348. ; 11:2, s. 267-281
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Tidskriftsartikel (refereegranskat)abstract
- Space weather is a phenomenon in which radioactivity and atomic particles is caused by emission from the Sun and stars. It is one of the extreme climate events that could potentially has short-term and long-term impacts on infrastructure. The effects of this phenomenon are a multi-fold process that include electronic system, equipment and component failures, short-term and long-term hazards and consequences to astronauts and aircraft crews, electrostatic charge variation of satellites, disruptions in telecommunications systems, navigational systems, power transmission failures and disturbances to the rail traffic and power grids. The critical infrastructures are becoming interdependent to each other and these infrastructures are vulnerable if one of them is affected due to space weather. Railway infrastructure could be affected by the extreme space weather events and long-term evolution due to direct and indirect effects on system components, such as track circuits, electronic components in-built in signalling systems or indirectly via interdependencies on power, communications, etc. While several space weather-related studies focus on power grids, Global Navigation Satellite System (GNSS) and aviation sectors, a little attention has focused towards probability of railway infrastructure disruptions. Nevertheless, disruptions due to space weather on signalling and train control systems has documented but other systems that railway infrastructure dependent upon are not very well studied. Due to the advancements in digitalization, cloud storage, Internet of Things (IoT), etc., that are embedded with electronic equipment are also possible to prone to these effects and it is even become more susceptible to the extreme space weather events. This paper gives a review of space weather effects on railways and other transportation systems and provide some of the mitigation measures to the infrastructure and societal point of view.
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| 37. |
- Torres Farinha, José, et al.
(författare)
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Life Cycle Cost versus Life Cycle Investment - A New Approach
- 2020
-
Ingår i: WSEAS transactions on systems and control. - : World Scientific and Engineering Academy and Society. - 1991-8763 .- 2224-2856. ; 15, s. 743-753
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Tidskriftsartikel (refereegranskat)abstract
- The paper proposes a model for the life cycle of physical assets that includes the maintenance policy, because it has direct implications on the equipment’s Return On Investment (ROI) and Life Cycle Cost; the developed model can be applied to any type of physical asset. The model is called Life Cycle Investment (LCI) instead of the traditional Life Cycle Cost (LCC). The paper proposes a new methodology based on the modified economic life cycle and lifespan methods by including the maintenance policy using maintenance Key Performance Indicators (KPI), namely Availability, based on the Mean Time Between Failures (MTBF) and the Mean Time To Repair (MTTR). The benefits (profits) that result from the asset’s Availability must be balanced with the initial investment and the variable maintenance investment along its life, which has relation with the maintenance policy and the ROI.
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| 38. |
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| 39. |
- Vila Forteza, Marc, et al.
(författare)
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New paradigms in Maintenance, operation, and health management of rotating machinery large fleets. The effect of Industry 4.0
- 2022
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Ingår i: 18th International Conference on Condition Monitoring and Asset Management (CM 2022). - : British Institute of Non-Destructive Testing (BINDT). ; , s. 311-321
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Konferensbidrag (refereegranskat)abstract
- Rotating machinery belong to the category of major equipment in many large industries as oil refineries. When such assets are installed in an industrial plant, they are expected to perform with minimal faults and failures guaranteeing that the plant can be operated within pre-defined reliability, safety, availability, and performance specifications. This paper provides an insight into current practices when dealing with large fleets of rotating machines in an Industry 4.0 context and what opportunities and challenges are encountered towards improving their safe operation and reliability by taking advantage of the development of new technologies.Bearing in mind that centrifugal pumps are the most common rotating machines in oil refineries, this paper is specially focused in this case, but its guidelines can be applied to all types of rotating equipment installed in an industrial plant.
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| 40. |
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