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research-article

A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines

Authors:

Rodrigo de Paula Monteiro,

Mariela Cerrada Lozada,

Diego Roman Cabrera Mendieta,

René Vinicio Sánchez Loja,

Carmelo José Albanez Bastos FilhoAuthors Info & Claims

Volume 204, Issue C

https://doi.org/10.1016/j.eswa.2022.117528

Published: 15 October 2022 Publication History

Abstract

Anomaly detection in time series is an important task to many applications, e.g, the maintenance policies of rotating machines within industries strongly rely on time series monitoring. Rotating machines are vital elements within industries. Therefore, maintenance policies on these critical elements concern the quality of products and safety issues. Condition-based maintenance is an example of those policies. In this context, we propose a novel method to train a deep learning-based feature extractor for the anomaly detection problem on rotating machinery. It consists of using a prototype selection algorithm to improve the training process of a randomly initialized feature extractor. We perform this process iteratively using data belonging to one probability distribution, i.e., the normal class. We carried the prototype selection out with the Nearest Neighbors algorithm, and the feature extractor was a Convolutional Neural Network. We validate the method on three datasets of spectrograms related to gearbox and compressors faults and achieved promising results. We obtained detection rates in anomalous data close to 100%, and the anomaly detectors classified normal instances with accuracy values superior to 95%. Those results were competitive concerning other deep learning-based anomaly detectors in the literature, with the advantage of being an integrated solution.

Highlights

•

Learning features for anomaly detection problems may be a challenging task.

•

Prototype selection improves the training of feature extractors for anomaly detection.

•

It helps mapping normal instances to a more restricted region of the feature space.

•

It makes the anomaly detection via one-class classification easier.

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Cited By

Li WLiu XWang DLu WYuan BQin CCheng YCăleanu C(2024)MITDCNNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121797238:PAOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121797
Li ZGe YYue XMeng L(2024)MCAD: Multi-classification anomaly detection with relational knowledge distillationNeural Computing and Applications10.1007/s00521-024-09838-036:23(14543-14557)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09838-0
Li YChen WChen BWang DTian LZhou MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Prototype-oriented unsupervised anomaly detection for multivariate time seriesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619209(19407-19424)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619209

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A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines

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Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 204, Issue C

Oct 2022

1098 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 October 2022

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Cited By

Li WLiu XWang DLu WYuan BQin CCheng YCăleanu C(2024)MITDCNNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121797238:PAOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121797
Li ZGe YYue XMeng L(2024)MCAD: Multi-classification anomaly detection with relational knowledge distillationNeural Computing and Applications10.1007/s00521-024-09838-036:23(14543-14557)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09838-0
Li YChen WChen BWang DTian LZhou MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Prototype-oriented unsupervised anomaly detection for multivariate time seriesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619209(19407-19424)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619209
Yang ZHuang YNazeer FZi YValentino GLi CLong JHuang H(2023)A novel fault detection method for rotating machinery based on self-supervised contrastive representationsComputers in Industry10.1016/j.compind.2023.103878147:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.compind.2023.103878

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