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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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2661-8907 2662-995X 2661-8907
DOI:	10.1007/s42979-024-02635-5