Abstract
A novel ensemble Yu’s norm-based deep metric learning (DMLYu) is proposed to diagnose the fault of rolling bearing in this paper, which can diagnose the fault classes through the information fusion method that combines the different diagnosis results produced by several Yu’s norm-based deep metric learning models with different scale signals. The suggested method is composed of three steps: firstly the vibration signal is decomposed into multiple IMF components by the EEMD method, then these IMF components are input into the DMLYu models which is called the modified deep metric learning model based on Yu’s norm-based similarity measure, respectively, to extract the feature parameters to diagnose the fault of rolling bearings from the different scales, and finally the final diagnosis decision is made by fusion strategy based on Bayesian belief method (BBM). At last, through a multifaceted diagnosis test of rolling bearing on different datasets, the effectiveness of the proposed ensemble DMLYu based on BBM is verified, and the superiority of the proposed diagnosis method is validated by comparing its diagnosis accuracy and generalization with DMLYu based on voting method and the individual DMLYu model.
Highlights
A novel ensemble DML based on Yu’s norm similarity measure (DMLYu) model based on Bayesian belief method (BBM) and ensemble empirical mode decomposition (EEMD) is proposed and applied to the fault diagnosis of the rolling bearings
In order to solve the misdiagnosis problem of data sample in the overlapping region at the boundary of different fault classes and improve the diagnosis accuracy and robustness of deep metric learning model, by the EEMD method the original vibration data of rolling bearing is decomposed into multiple intrinsic mode functions (IMFs) components which are input into the deep metric learning model based on Yu’s norm, respectively, the initial fault results are obtained, respectively, and at last the final diagnosis decision is made by the BBM fusion strategy
Rough a multifaceted comparison of three methods on different experimental datasets, the effectiveness and generalization of the proposed ensemble DMLYu model based on BBM were verified by comparison with the ensemble DMLYu model based on voting method and individual DMLYu model. e diagnosis results have demonstrated that the proposed ensemble method was more effective and robust than other ensemble DMLYu models based on voting method and individual DMLYu model for fault diagnosis of rolling bearings under different working conditions and verified that the proposed ensemble method can diagnose the fault of rolling bearings with high accuracy and reliability
Summary
Owing to the fact that ensemble deep metric learning model inherits the advantages of both the deep metric learning models and the ensemble learning, the ensemble deep metric learning has better generalization performance and higher diagnosis accuracy. It can compute the feature representation h(N) of a data sample x by passing it to multiple-layer nonlinear transformations and map the original feature parameters to discriminative feature space by maximizing interclass variation and minimizing intraclass variation [15, 16]. The Euclidean distance of the data sample points xi and xj in the deep metric network space is substituted by the similarity based on Yu’s norm which is written as follows: df(n) Xi, Xj S < f(n) Xi, f(n)Xj > ,. E corresponding general diagnosis procedure is summarized as follows: Step 1: collecting the data samples of different fault classes of rolling bearings by the sliding window from the vibration data. Step 4: using the ensemble DMLYu model to diagnose the testing data sample
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