A novel vibro-acoustic fault diagnosis method of rolling bearings via entropy-weighted nuisance attribute projection and orthogonal locality preserving projections under various operating conditions. (July 2022)

• Record Type: Journal Article
• Authors: Yang, Di; Lv, Yong; Yuan, Rui; Yang, Ke; Zhong, Hongyu
• Is Part Of: Applied acoustics. Volume 196(2022)
• Publication Date: 2022-07
• Subjects: Entropy-weighted nuisance attribute projection -- Orthogonal locality preserving projection -- Feature fusion -- Acoustics -- Fault diagnosis; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2022.108889
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: Propose entropy-weighted nuisance attribute projection (EWNAP) to solve the loss of available information caused by the weight matrix in nuisance attribute projection (NAP) only taking two values. Introduce EWNAP into the field of fault diagnosis of rolling bearings to alleviate interferences of redundant attributes, including the interferences of various operating conditions and environmental noises. Adopt Orthogonal locality preserving projections (OLPP) to extract the sensitive features embedded in the high-dimensional feature matrix processed by EWNAP. Construct a novel fault diagnosis model via EWNAP, OLPP, and neural network to achieve vibro-acoustic fault diagnosis of rolling bearings under various operating conditions. Abstract: Accurate fault pattern recognition under various operating conditions is a huge challenge for vibro-acoustic fault diagnosis of rolling bearings. Traditional feature fusion methods are difficult to extract sensitive features related to faults under various operating conditions. To solve the problem, a novel feature fusion method is proposed in this paper based on entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP). Specifically, the method mainly includes three steps: feature extraction, alleviating interference of operating condition, and fusion feature. First, features are extracted from the acquired sound signals. Second, by introducing the covariance matrix of the acquired signals and fuzzy entropy to improve the weighted matrix of nuisance attribute projection (NAP), the EWNAP is proposed to reduce the impact of operating conditions in these extracted features. In the end, OLPP is adopted to fuse features and obtain sensitive features. The clustering performance of the proposed method is quantitatively described by an evaluation index, and for fault pattern recognition, the vectors as samples, which are composed of features extracted via EWNAP-OLPP, are fed into the back propagation (BP) neural network. The analysis on a fault case of rolling bearings shows that the proposed method is robust for vibro-acoustic fault diagnosis of rolling bearings under various operating conditions and superior to traditional methods.