A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing

• Published in: Quality and reliability engineering international Vol. 40; no. 8; pp. 4131 - 4146
• Main Authors: Yang, Yuan‐Jian, Ma, Chengyuan, Liu, Gui‐Hua, Lu, Hao, Dai, Le, Wan, Jia‐Lun, Guo, Junyu
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.12.2024
• Subjects: Artificial neural networks; Bearings; CBAM; CNN; Continuous wavelet transform; Failure times; health index; Maintenance costs; Network reliability; Performance degradation; Reliability analysis; reliability assessment; Roller bearings; Wavelet transforms; Wiener process
• ISSN: 0748-8017; 1099-1638
• DOI: 10.1002/qre.3630

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings’ reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method.