Recognizing Bearings’ Degradation Stage Using Multimodal Autoencoder to Learn Features from Different Time Series

Utilizing machine learning technologies to monitor assets’ health conditions can improve the effectiveness of maintenance activities. However, accurately recognizing the current health degradation stages of industrial assets requires a time-consuming manual feature extraction due to the wide range o...

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Vydáno v:	SN computer science Ročník 5; číslo 4; s. 371
Hlavní autoři:	Alfeo, Antonio Luca, Cimino, Mario G. C. A., Gagliardi, Guido
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.04.2024 Springer Nature B.V
Témata:	Algorithms Classification Clustering Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data integration Data Structures and Information Theory Deep learning Degradation Design Discriminant analysis Effectiveness Feature extraction Information Systems and Communication Service Innovative Intelligent Industrial Production and Logistics 2022 Machine learning Neural networks Original Research Pattern Recognition and Graphics Preventive maintenance Software Engineering/Programming and Operating Systems State of the art Time series Vibration Vision Deep feature learning Data fusion Multimodal autoencoder Contrastive learning Predictive maintenance Smart manufacturing
ISSN:	2661-8907, 2662-995X, 2661-8907
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Abstract	Utilizing machine learning technologies to monitor assets’ health conditions can improve the effectiveness of maintenance activities. However, accurately recognizing the current health degradation stages of industrial assets requires a time-consuming manual feature extraction due to the wide range of observable measures (e.g., temperature, vibration) and behaviors characterizing assets’ degradation. To address this issue, feature learning technology can transform minimally processed time series into informative features, i.e., able to simplify the classification task (e.g., recognizing degradation stages) regardless of the specific machine learning classifier employed. In this work, minimally preprocessed time series of vibration and temperature of industrial bearings are exploited by an autoencoder-based architecture to extract degradation-representative features to be used for recognizing their degradation stages. Different autoencoder architectures are employed to compare their data fusion strategies. The effectiveness of the proposed approach is evaluated in terms of recognition performance and the quality of the learned features by using a publicly available real-world dataset and comparing the proposed approach against a state-of-the-art feature learning technology. We tested three different multimodal autoencoder-based feature learning approaches, i.e., shared-input autoencoder (SAE), multimodal autoencoder (MMAE), and partition-based autoencoder (PAE). All the AE-based architecture results in classification performances greater or comparable with the state-of-the-art feature learning technology, despite being trained in an unsupervised fashion. Also, the features provided via PAE correspond to the greatest performances in recognizing bearings’ degradation stage, providing high-quality features both from a classification and clustering perspective. Unsupervised feature learning methodologies based on multimodal autoencoders are capable of learning high-quality features. These result in greater degradation stages recognition performances when compared to supervised state-of-the-art feature learning technology. Also, this enables the correct representation of the expected progressive degradation of the bearing.
AbstractList	Utilizing machine learning technologies to monitor assets’ health conditions can improve the effectiveness of maintenance activities. However, accurately recognizing the current health degradation stages of industrial assets requires a time-consuming manual feature extraction due to the wide range of observable measures (e.g., temperature, vibration) and behaviors characterizing assets’ degradation. To address this issue, feature learning technology can transform minimally processed time series into informative features, i.e., able to simplify the classification task (e.g., recognizing degradation stages) regardless of the specific machine learning classifier employed. In this work, minimally preprocessed time series of vibration and temperature of industrial bearings are exploited by an autoencoder-based architecture to extract degradation-representative features to be used for recognizing their degradation stages. Different autoencoder architectures are employed to compare their data fusion strategies. The effectiveness of the proposed approach is evaluated in terms of recognition performance and the quality of the learned features by using a publicly available real-world dataset and comparing the proposed approach against a state-of-the-art feature learning technology. We tested three different multimodal autoencoder-based feature learning approaches, i.e., shared-input autoencoder (SAE), multimodal autoencoder (MMAE), and partition-based autoencoder (PAE). All the AE-based architecture results in classification performances greater or comparable with the state-of-the-art feature learning technology, despite being trained in an unsupervised fashion. Also, the features provided via PAE correspond to the greatest performances in recognizing bearings’ degradation stage, providing high-quality features both from a classification and clustering perspective. Unsupervised feature learning methodologies based on multimodal autoencoders are capable of learning high-quality features. These result in greater degradation stages recognition performances when compared to supervised state-of-the-art feature learning technology. Also, this enables the correct representation of the expected progressive degradation of the bearing.
ArticleNumber	371
Author	Gagliardi, Guido Cimino, Mario G. C. A. Alfeo, Antonio Luca
Author_xml	– sequence: 1 givenname: Antonio Luca surname: Alfeo fullname: Alfeo, Antonio Luca organization: Dept. of Information Engineering, University of Pisa, Research Center E. Piaggio, University of Pisa – sequence: 2 givenname: Mario G. C. A. surname: Cimino fullname: Cimino, Mario G. C. A. organization: Dept. of Information Engineering, University of Pisa, Research Center E. Piaggio, University of Pisa – sequence: 3 givenname: Guido orcidid: 0000-0003-2020-6439 surname: Gagliardi fullname: Gagliardi, Guido email: guido.gagliardi@phd.unipi.it organization: Dept. of Information Engineering, University of Pisa, Dept. of Information Engineering, University of Florence, Dept. of Electrical Engineering, KU Leuven
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Cites_doi	10.1016/j.jmsy.2021.05.003 10.1145/3347711 10.1109/TIM.2019.2917735 10.1109/TMECH.2021.3053173 10.1016/j.eswa.2013.02.025 10.1016/j.jmsy.2020.07.008 10.1109/TII.2017.2670505 10.1109/TII.2018.2793246 10.1162/089976600300014980 10.1007/s10845-021-01797-w 10.1080/00207543.2020.1836419 10.1115/1.4052364 10.1145/3301282 10.1162/089976698300017467 10.1007/s41066-022-00357-8 10.1109/TPAMI.2013.50 10.1016/j.neucom.2020.11.048 10.1016/j.ymssp.2017.11.016 10.1109/ACCESS.2019.2963092 10.1162/neco_a_01273 10.1109/ACCESS.2023.3268233 10.1109/TIM.2012.2212508 10.1186/s40537-021-00508-9 10.1177/1475921719893594 10.1109/ECCE.2018.8557651 10.1007/978-3-030-43353-6_8 10.1109/SSCI52147.2023.10371895 10.1145/1553374.1553511 10.36001/phmconf.2015.v7i1.2655 10.5220/0011548000003329 10.1016/j.chemolab.2022.104711 10.1007/978-1-4842-3913-1_8 10.1007/978-3-030-67270-6_5 10.1007/978-3-031-18050-7_2 10.1109/NER52421.2023.10123758 10.1109/ICTAI.2011.171
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SubjectTerms	Algorithms Classification Clustering Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data integration Data Structures and Information Theory Deep learning Degradation Design Discriminant analysis Effectiveness Feature extraction Information Systems and Communication Service Innovative Intelligent Industrial Production and Logistics 2022 Machine learning Neural networks Original Research Pattern Recognition and Graphics Preventive maintenance Software Engineering/Programming and Operating Systems State of the art Time series Vibration Vision
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Title	Recognizing Bearings’ Degradation Stage Using Multimodal Autoencoder to Learn Features from Different Time Series
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