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

• Main Authors: Mohammadreza Kaji, Jamshid Parvizian, Hans Wernher van de Venn
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Applied Sciences
• Subjects: health indicator; performance degradation assessment; deep learning; vibration monitoring; bearing; remaining useful life
• Online Access: https://www.mdpi.com/2076-3417/10/24/8948

Estimating the remaining useful life (RUL) of components is a crucial task to enhance 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 in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the , most of the existing methods are based on manual feature extraction techniques and require 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 was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (<i>MD</i>) between the healthy and failure stages was measured as the . The proposed method was tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.