| 1. |
- Assenza, Giacomo, et al.
(författare)
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White Paper on Industry Experiences in Critical Information Infrastructure Security : A Special Session at CRITIS 2019
- 2020
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Ingår i: Critical Information Infrastructures Security14th International Conference, CRITIS 2019. - Cham : Springer. - 9783030376697 - 9783030376703 ; , s. 197-207
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Bokkapitel (refereegranskat)abstract
- The security of critical infrastructures is of paramount importance nowadays due to the growing complexity of components and applications. This paper collects the contributions to the industry dissemination session within the 14th International Conference on Critical Information Infrastructures Security (CRITIS 2019). As such, it provides an overview of recent practical experience reports in the field of critical infrastructure protection (CIP), involving major industry players. The set of cases reported in this paper includes the usage of serious gaming for training infrastructure operators, integrated safety and security management in the chemical/process industry, risks related to the cyber-economy for energy suppliers, smart troubleshooting in the Internet of Things (IoT), as well as intrusion detection in power distribution Supervisory Control And Data Acquisition (SCADA). The session has been organized to stimulate an open scientific discussion about industry challenges, open issues and future opportunities in CIP research.
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| 2. |
- De Donato, Lorenzo, et al.
(författare)
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Towards AI-assisted digital twins for smart railways : preliminary guideline and reference architecture
- 2023
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Ingår i: Journal of Reliable Intelligent Environments. - : Springer Science and Business Media Deutschland GmbH. - 2199-4668 .- 2199-4676.
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Tidskriftsartikel (refereegranskat)abstract
- In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.
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| 3. |
- Dirnfeld, Ruth, et al.
(författare)
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Integrating AI and DTs : challenges and opportunities in railway maintenance application and beyond
- 2024
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Ingår i: Simulation (San Diego, Calif.). - : Sage Publications. - 0037-5497 .- 1741-3133.
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Tidskriftsartikel (refereegranskat)abstract
- In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber-physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.
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| 4. |
- Dirnfeld, Ruth, et al.
(författare)
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Railway Digital Twins and Artificial Intelligence : Challenges and Design Guidelines
- 2022
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Ingår i: Dependable Computing – EDCC 2022 Workshops. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031162442 - 9783031162459 ; , s. 102-113
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Konferensbidrag (refereegranskat)abstract
- In the last years, there has been a growing interest in the emerging concept of Digital Twins (DTs) among software engineers and researchers. DTs represent a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems. They can play a key role in different domains, as it is also witnessed by several ongoing standardisation activities. However, several challenging issues have to be faced in order to effectively adopt DTs, in particular when dealing with critical systems. This work provides a review of the scientific literature on DTs in the railway sector, with a special focus on their relationship with Artificial Intelligence. Challenges and opportunities for the usage of DTs in railways have been identified, with interoperability being the most discussed challenge. One difficulty is to transmit operational data in real-time from edge systems to the cloud in order to achieve timely decision making. We also provide some guidelines to support the design of DTs with a focus on machine learning for railway maintenance.
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| 5. |
- Edrisi, Farid, et al.
(författare)
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Digital Twin for Sustainability Assessment and Policy Evaluation : A Systematic Literature Review
- 2023
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Ingår i: 2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS). - : IEEE. - 9798350312386 - 9798350312393 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Digital Twin is an emerging technology that is used for different purposes, e.g monitoring, optimization, prediction, etc., in a wide range of real-world applications. Manufacturing is the most prevalent industry employing digital twin technology to achieve sustainability through enhancing smartness and intelligence. In this regard, several literature reviews have been established on the digital twin's role in sustainable manufacturing development. However, despite the importance of assessment and evaluation of developed sustainable actions and policies, and the high capability of the digital twin concept to support it, there is a lack of effort to systematically review the current state-of-the-art on the contribution of the digital twin in sustainability assessment and policy evaluation. By conducting a systematic literature review, this paper seeks to close this gap. By applying inclusion and exclusion criteria, 12 relevant papers are identified to be analyzed in more detail. The results show the ongoing effort on developing architectural frameworks and cutting-edge methodologies for integrating Digital Twin with conventional sustainability assessment and policy evaluation approaches. However, its potential benefits are not fully utilized, as evidenced by the limited effort put forth in this direction.
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| 6. |
- Rajabi, Saeed, et al.
(författare)
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Automated fault diagnosis of rolling element bearings based on morphological operators and M-ANFIS
- 2016
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Ingår i: 24th Iranian Conference on Electrical Engineering (ICEE). - : IEEE Press. - 9781467387903 - 9781467387897 ; , s. 1757-1762
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Konferensbidrag (refereegranskat)abstract
- Condition monitoring and fault diagnosis of rolling element bearings (REBs) are at present very important to ensure the reliability of rotating machinery. This paper presents a new pattern classification approach for bearings diagnostics, which combines Mathematical Morphology (MM) and Multi-output Adaptive Neuro Fuzzy Inference System (M-ANFIS) classifier. MM is used for filtering Vibration signals, which acquired through the accelerometers mounted on the bearing housing. In this regard, to have an effective morphological operator, the structure elements (SEs) are selected based on the Kurtosis value. Then, to design an automated fault diagnosis structure, the features of this filtered signal, are extracted and used in the M-ANFIS model to learn and classify the bearing condition. The MM method overcomes the drawbacks of other signal processing methods and the M-ANFIS model can handle variation conditions. The experimental results indicate that the proposed strategy not only reduces the error rate but also is robust to changes of load, speed and size of defects.
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| 7. |
- Rajabi, S., et al.
(författare)
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Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier
- 2022
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 206
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Tidskriftsartikel (refereegranskat)abstract
- Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing's condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method's capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.
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| 8. |
- Saman Azari, Mehdi, et al.
(författare)
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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 12887-12910
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Forskningsöversikt (refereegranskat)abstract
- The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.
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| 9. |
- Saman Azari, Mehdi, et al.
(författare)
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Data-Driven Fault Diagnosis of Once-through Benson Boilers
- 2019
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Ingår i: 2019 4th International Conference on System Reliability and Safety (ICSRS). - : IEEE Press. - 9781728147819 - 9781728147802 ; , s. 345-354
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Konferensbidrag (refereegranskat)abstract
- Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario.
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| 10. |
- Saman Azari, Mehdi, et al.
(författare)
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Fault diagnosis of an industrial once-through benson boiler by utilizing adaptive neuro-fuzzy inference system
- 2016
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Ingår i: 2016 6th Conference on Thermal Power Plants (CTPP). - : IEEE. - 9781509003723 ; , s. 32-37
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Konferensbidrag (refereegranskat)abstract
- Nowadays, the increasing request to have more electric power and the growing complexity of advanced thermal power systems, make it ever more important to improve the performance and reliability of the systems. Hence, an attention is concentrated on fault diagnosis systems to compensate the adverse effects automatically, under conditions of noisy measurement. In order to improve the proficiency of process monitoring and increase accuracy of fault diagnosis (FD) for the once-through Benson type boiler, this article proposed a data driven method based on the configuration of six adaptive neuro fuzzy inference systems (ANFIS). In the proposed structure, due to strong interaction between measurements each ANFIS classifier has been developed to diagnose one particular fault. Finally to evaluate the effectiveness and performance of the proposed FD system against 6 major faults of once-through Benson type boiler under conditions of noisy measurement, different set of test scenarios have been performed.
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| 11. |
- Saman Azari, Mehdi, et al.
(författare)
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Improving Resilience in Cyber-Physical Systems based on Transfer Learning
- 2022
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Ingår i: 2022 IEEE International Conference on Cyber Security and Resilience (CSR). - : IEEE. - 9781665499521 - 9781665499538 ; , s. 203-208
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Konferensbidrag (refereegranskat)abstract
- An essential aspect of resilience within Cyber-Physical Systems stands in their capacity of early detection of faults before they generate failures. Faults can be of any origin, either natural or intentional. Detection of faults enables predictive maintenance, where faults are managed through diagnosis and prognosis. In this paper we focus on intelligent predictive maintenance based on a class of machine learning techniques, namely transfer learning, which overcomes some limitations of traditional approaches in terms of availability of appropriate training datasets and discrepancy of data distribution. We provide a conceptual approach and a reference architecture supporting transfer learning within intelligent predictive maintenance applications for cyber-physical systems. The approach is based on the emerging paradigms of Industry 4.0, the industrial Internet of Things, and Digital Twins hosting run-time models for providing the training data set for the target domain. Although we mainly focus on health monitoring and prognostics of industrial machinery as a reference application, the general approach is suitable to both physical- and cyber-threat detection, and to any combination of them within the same system, or even in complex systems-of-systems such as critical infrastructures. We show how transfer learning can aid predictive maintenance with intelligent fault detection, diagnosis and prognosis, and describe some the challenges that need to be addressed for its effective adoption in real industrial applications.
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| 12. |
- Saman Azari, Mehdi, et al.
(författare)
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Using One-Class Support Vector Machine for the Fault Diagnosis of an Industrial Once-Through Benson Boiler
- 2012
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Ingår i: The Modares Journal of Electrical Engineering. - : Tarbiat Modares University Press. - 2228-527X. ; 12:3, s. 39-45
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Tidskriftsartikel (refereegranskat)abstract
- Considering that once-through Benson boiler is one of the most crucial equipment of a thermal power plant, occurrence of any fault in its different parts can lead to decrease of the performance of system, and even may cause system damage and endanger the human life. In this paper, due to the high complexity of the system's dynamic equations, we utilized data-based method for diagnosing the faults of the once-through Benson boiler. In order to enhance the fault diagnose (FD) system proficiency and also due to strong interactions between measurements, we decided to utilize six one-class support vector machine (SVM) algorithms to diagnose six major faults of once-through Benson boiler. In the proposed structure, each One-class SVM algorithm has been developed to diagnose one special fault. Finally, we carry out diverse test scenarios in different states of fault occurrence to evaluate the performance of the proposed FD system against the six major faults of the once-through Benson Boiler under conditions of noisy measurement.
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| 13. |
- Singh, Prasannjeet, et al.
(författare)
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Towards self-healing in the internet of things by log analytics and process mining
- 2020
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Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 4644-4651
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing. © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
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| 14. |
- Singh, Prasannjeet, et al.
(författare)
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Using log analytics and process mining to enable self-healing in the Internet of Things
- 2022
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Ingår i: Environment Systems and Decisions. - : Springer. - 2194-5403 .- 2194-5411. ; 42:2, s. 234-250
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT.
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