Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry

Davari, Narjes; Veloso, Bruno; Ribeiro, Rita P.; Pereira, P.; Gama, João

doi:10.1109/dsaa53316.2021.9564181

Cited by 27 publications

(18 citation statements)

References 18 publications

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“…In the first, presented by [1], the authors constructed a rule-based system to produce some alerts about the state of the compressor. The second work, presented by [3], explores the usage of deep learning auto encoders to produce alerts. In both cases, the results are satisfactory, but there is a vast space to improve accuracy and explanation.…”

Section: Methodsmentioning

confidence: 99%

A Benchmark dataset for predictive maintenance

Veloso¹,

Gama²,

Ribeiro³

et al. 2022

Preprint

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The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

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Section: Methodsmentioning

confidence: 99%

A Benchmark dataset for predictive maintenance

Veloso¹,

Gama²,

Ribeiro³

et al. 2022

Preprint

Self Cite

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“…Weber and Schütte 133 Big data and cognitive computing AI is the study of attempting to teach robots to utilise language, develop abstractions and concepts, and solve issues that are currently reserved for humans (p. 3). 145 DL System level Fault diagnosis Hermawan et al 146 CNN, LSTM System level RUL Putra et al 73 ML Vehicle level RUL Markridis et al 72 ML Vehicle level Fault diagnosis Chen et al 147 DCNN, NB Vehicle level Fault diagnosis Ezhilirasu and Jennions 74 ML Vehicle level Fault diagnosis intelligence (AI), the selection of AI techniques should be based on the desired outcomes. It is crucial to think about the intended results and select the AI techniques that are most likely to achieve them in order to successfully apply AI in maintenance.…”

Section: Business and Managementmentioning

confidence: 99%

Platform health management for aircraft maintenance – a review

Kwakye,

Jennions,

Ezhilarasu

2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions.

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“…Michau et al [12] used AE network for unsupervised feature parameter learning and integrated it with a one-class classifier that is only trained with samples of healthy conditions for fault detection. Davari et al [4] proposed a data-driven predictive maintenance framework for the air production unit system of a train by deep learning based on a sparse AE network that efficiently detects abnormal data and considerably reduces the false alarm rate.…”

Section: Related Workmentioning

confidence: 99%

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

Davari

Pashami

Veloso

et al. 2022

Lecture Notes in Computer Science

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This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.

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Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry

Cited by 27 publications

References 18 publications

A Benchmark dataset for predictive maintenance

A Benchmark dataset for predictive maintenance

Platform health management for aircraft maintenance – a review

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

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