Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

Version 1 : Received: 23 November 2020 / Approved: 23 November 2020 / Online: 23 November 2020 (14:34:58 CET)
Version 2 : Received: 11 December 2020 / Approved: 11 December 2020 / Online: 11 December 2020 (11:59:20 CET)

A peer-reviewed article of this Preprint also exists.

Kaji, M.; Parvizian, J.; Venn, H.W. Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform. Appl. Sci. 2020, 10, 8948. Kaji, M.; Parvizian, J.; Venn, H.W. Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform. Appl. Sci. 2020, 10, 8948.

Journal reference: Appl. Sci. 2020, 10, 8948
DOI: 10.3390/app10248948

Abstract

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the , most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model is trained by the healthy operation dataset. Finally, the Mahalanobis Distance (MD) between the healthy and failure stages is measured as the . The proposed method is tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

Supplementary and Associated Material

https://drive.google.com/drive/folders/1HxLQ-L04V0aT7JcNjgVcIPAK0hop85aE?usp=sharing: IMS raw vibration signal, all of the python codes, and the excel files

Subject Areas

health indicator; performance degradation assessment; deep learning; vibration monitoring; bearing; remaining useful life; digital twin

Comments (1)

Comment 1
Received: 11 December 2020
Commenter: Hans Wernher van de Venn
Commenter's Conflict of Interests: Author
Comment: Minor revision of the entire paper due to reviewer's comments
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.