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)
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.
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.
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.
health indicator; performance degradation assessment; deep learning; vibration monitoring; bearing; remaining useful life; digital twin
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Hans Wernher van de Venn
Commenter's Conflict of Interests: Author