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Open AccessArticle

Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis

by Long Zhang, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu and Yu Qiao

Sensors 2022, 22(24), 9759; https://doi.org/10.3390/s22249759 - 13 Dec 2022

Cited by 4 | Viewed by 2109

Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis. Full article

(This article belongs to the Special Issue Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022)

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12 pages, 3491 KB

Open AccessArticle

A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions

by Huijie Ma, Shunming Li and Zenghui An

Appl. Sci. 2019, 9(8), 1603; https://doi.org/10.3390/app9081603 - 17 Apr 2019

Cited by 20 | Viewed by 3610

Abstract

Intelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault features. Inspired by speech recognition, the nuisance attribute projection method in speech recognition is introduced into fault diagnosis to solve the problem of feature extraction in variable speed signals. Based on the idea of unsupervised feature learning, the loss function of nuisance attribute projection is added to the loss function of convolutional neural network (CNN) to learn fault features from original data. Health status is classified according to the learned characteristics and projection matrix P. A special designed bearing dataset is employed to verify the effectiveness of the proposed method. The results show that the proposed method has a higher accuracy and a simpler framework, which is superior to the existing methods in bearing fault diagnosis. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)

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