A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

Abstract

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

Industrial Process Diagnosis based on Information-based Features

“…Very often, the model of the source or the hypothesis of data processing are not met, as is the case in many industrial environments. The informationbased domain is exemplified with some examples as diversity entropy, [21], Kullbak-Leibler divergence, [22], dispersion entropy, [23], information fusion, [24], and probabilistic frameworks [25], [26].…”

A new rotating machinery fault diagnosis method for different speeds based on improved multivariate multiscale fuzzy distribution entropy

“…To solve the limitations in the coarse-graining process, several modified multiscale entropy have been proposed by various scale extraction frameworks based on multiscale entropy, such as composite entropy [18,19], generalized entropy [20], refined composite entropy [21,22]. However, these improved methods need much more time than the original method, meanwhile the maximum scale of these methods is selected by experience.…”

Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis

“…As a result, each fault creates different changes in signal complexity at different time scales. Hence, measuring signal complexity by calculating entropy over different scales (multiscale algorithms) is extensively applied to bearing fault diagnoses [13][14][15].…”