| 1. |
- Fornlöf, Veronica, 1984-, et al.
(författare)
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On-Condition Parts Versus Life Limited Parts : A Trade off in Aircraft Engines
- 2016
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Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319235967 - 9783319235974 ; , s. 253-262
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Konferensbidrag (refereegranskat)abstract
- Maintaining an aircraft engine is both complex and time consuming since an aircraft is an advanced system with high demands on safety and reliability. Each maintenance occasion must be as effective as possible and the maintenance need to be executed without performing excessive maintenance. The aim of this paper is to describe the essence of aircraft engine maintenance and to point out the potential for improvement within the maintenance planning by improving the remaining life predictions of the On-Condition parts, i.e. parts that are not given a fixed life limit.
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| 2. |
- Galar, Diego, et al.
(författare)
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Big Data in Asset Management : Knowledge Discovery in Asset Data by the Means of Data Mining
- 2016
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Ingår i: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015). - Cham : Springer. - 9783319270623 - 9783319270647 - 3319270648 ; , s. 161-171
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Konferensbidrag (refereegranskat)abstract
- Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.
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| 3. |
- Gerdes, Mike, et al.
(författare)
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Decision Trees and the Effects of Feature Extraction Parameters for Robust Sensor Network Design
- 2017
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Ingår i: Eksploatacja i Niezawodność – Maintenance and Reliability. - : Polskie Naukowo - Techniczne Towarzystwo Eksploatacyjne. - 1507-2711 .- 2956-3860. ; 19:1, s. 31-42
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Tidskriftsartikel (refereegranskat)abstract
- Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classy different errors with a 75% probability and how different feature extraction options influence the information gain.
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| 4. |
- Gerdes, Mike, et al.
(författare)
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Fuzzy condition monitoring of recirculation fans and filters
- 2016
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Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer. - 0975-6809 .- 0976-4348. ; 7:4, s. 469-479
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Tidskriftsartikel (refereegranskat)abstract
- A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.
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| 5. |
- Gerdes, M., et al.
(författare)
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Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning
- 2017
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Ingår i: Insight. - : British Institute of Non-Destructive Testing. - 1354-2575 .- 1754-4904. ; 59:8, s. 424-433
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Tidskriftsartikel (refereegranskat)abstract
- Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.
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| 6. |
- Linnéusson, Gary, et al.
(författare)
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In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing
- 2016. - 1
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Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety. - Cham : Springer. - 9783319235967 - 9783319235974 ; , s. 263-282
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The problem of maintenance consequential costs has to be dealt with in manufacturing and is core of this paper. The need of sustainable partnership between manufacturing and maintenance is addressed. Stuck in a best practice thinking, applying negotiation as a method based on power statements in the service level agreement, the common best possible achievable goal is put on risk. Instead, it may enforce narrow minded sub optimized thinking even though not intended so. Unfortunately, the state of origin is not straightforward business. Present maintenance cost modelling is approached, however limits to its ability to address the dynamic complexity of production flows are acknowledged. The practical problem to deal with is units put together in production flows; in which downtime in any unit may or may not result in decreased throughput depending on its set up. In this environment accounting consequential costs is a conundrum and a way forward is suggested. One major aspect in the matter is the inevitable need of shift in mind, from perspective thinking in maintenance and manufacturing respectively towards shared perspectives, nourishing an advantageous sustainable partnership.
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| 7. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Context Awareness in Predictive Maintenance
- 2016
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Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety<em></em>. - Cham : Springer. - 9783319235974 - 9783319235967 ; , s. 197-211
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Bokkapitel (refereegranskat)abstract
- Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.
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| 8. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Context preparation for predictive analytics – a case from manufacturing industry
- 2017
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Ingår i: Journal of Quality in Maintenance Engineering. - : Emerald Publishing Limited. - 1355-2511 .- 1758-7832. ; 23:3, s. 341-354
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Tidskriftsartikel (refereegranskat)abstract
- PurposeThe purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.Design/methodology/approachThis paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.FindingsMultiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.Originality/valueThis paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.
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| 9. |
- Supej, Matej, et al.
(författare)
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The Contribution of Ski Poles to Aerodynamic Drag in Alpine Skiing
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:14
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Tidskriftsartikel (refereegranskat)abstract
- The present study was designed to determine the contribution of the cross-sectional area of the ski poles (Sp) to the total aerodynamic drag during alpine skiing. At three different wind speeds in a wind tunnel, 10 skiers assumed typical alpine skiing postures (high, middle, and tuck), and their frontal aerodynamic drag was assessed with a force plate and their cross-sectional area, along with that of their ski poles, determined by interactive image segmentation. The data collected were utilized to examine intra-subject variation in Sp, the effects of Sp on the coefficient of aerodynamic drag (Cd), and the product of Cd and total cross-sectional area (Cd∙S. The major findings were as follows: (i) Sp ranged from 0.0067 (tuck position) to 0.0262 m2 (middle position), contributing 2.2–4.8% of the total cross-sectional area, respectively; (ii) Sp was dependent on wind speed in the high and middle positions; (iii) intra-subject variations ranged from 0.0018 m2 (27.6%) in the tuck position to 0.0072 m2 (30.5%) in the high position; (iv) Sp exerted a likely effect on Cd and Cd∙S. The extensive intra- and inter-skier variability in Sp can account for as much as ~5% of the total frontal cross-sectional area and future investigations on how elite skiers optimize their positioning of the poles in a manner that reduces aerodynamic drag are warranted.
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