A fault mode identification methodology based on self-organizing map

Schwartz, Sébastien; Jimenez, Juan José Montero; Salaün, Michel; Vingerhoeds, Rob A.

doi:10.1007/s00521-019-04692-x

Cited by 18 publications

(14 citation statements)

References 43 publications

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“…No error levels are measured to assess or to end the training process. These algorithms use other criteria to end the training process, such the number of training iterations or the progress of a convergence indicator over time [147]. Clustering is an example of tasks performed by unsupervised learning algorithms.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…The functional blocks presented in Section 2 present intuitively a configuration in series where a single model is used to fulfil each functional block. For example [147] presents a series configuration of SOM along with a statistics model using probability density function to address the functional blocks of degradation modelling and fault detection. Nevertheless, as complexity in the information or data increases, two complementary models could be used in series to fulfil a single functional block.…”

Section: Configurations For Multi-model Approachesmentioning

confidence: 99%

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Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Jimenez

Schwartz

Vingerhoeds

et al. 2020

Journal of Manufacturing Systems

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154

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The use of a modern technological system requires a good engineering approach, optimized operations, and proper maintenance in order to keep the system in an optimal state. Predictive maintenance focuses on the organization of maintenance actions according to the actual health state of the system, aiming at giving a precise indication of when a maintenance intervention will be necessary. Predictive maintenance is normally implemented by means of specialized computational systems that incorporate one of several models to fulfil diagnostics and prognostics tasks. As complexity of technological systems increases over time, single-model approaches hardly fulfil all functions and objectives for predictive maintenance systems. It is increasingly common to find research studies that combine different models in multi-model approaches to overcome complexity of predictive maintenance tasks, considering the advantages and disadvantages of each single model and trying to combine the best of them. These multi-model approaches have not been extensively addressed by previous review studies on predictive maintenance. Besides, many of the possible combinations for multi-model approaches remain unexplored in predictive maintenance applications; this offers a vast field of opportunities when architecting new predictive maintenance systems. This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Configurations For Multi-model Approachesmentioning

confidence: 99%

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Jimenez

Schwartz

Vingerhoeds

et al. 2020

Journal of Manufacturing Systems

Self Cite

154

View full text Add to dashboard Cite

show abstract

“…The different anomaly detection approaches are evaluated on the basis of the following performance indicators: accu-racy [45], anomaly detection rate (recall) [34], false alarm rate [34], and F1-score [45] which are expressed in ( 22)- (25), respectively. The precision indicator that is needed for calculating F1-score is expressed in (26) [45].…”

Section: B Implemented Scenariosmentioning

confidence: 99%

“…Furthermore, it does not need large training data to generalize [23], and it has the merits of interpretability and understandability [24]. SOM has been employed in various predictive maintenance applications, including anomaly detection [11], fault classification [25], and remaining useful life estimation [26].…”

Section: Introductionmentioning

confidence: 99%

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Assafo

Langendoerfer

2021

IEEE Access

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Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses the issues associated with multi-operating-condition TCM models, namely the varying discrimination ability of sensory features; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features.

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“…More recently, Schwartz et al [28] propose a method using self-organizing maps (SOMs) and kernel density estimation for fault detection and identification in aircraft jet engines. Baptista et al [29] study the use of hybrid neural networks combining RNN layers with multi-layer perceptron (MLP) layers to estimate the RUL.…”

Section: Applications To Aircraft Systems Health Monitoringmentioning

confidence: 99%

Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

et al. 2021

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Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. roposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.

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A fault mode identification methodology based on self-organizing map

Cited by 18 publications

References 43 publications

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

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