Abstract
This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary characteristics of the fault vibration signals. In our work, a novel entropy based method called generalized composite multiscale diversity entropy (GCMDiEn) is developed. This method can effectively track the inside pattern changes of the time series by the description of cosine similarity between adjacent orbits. Unlike most of the existing entropy based results which concentrate on the static orderliness, we analyze the dynamic complexity characteristics of the arbitrary time series. Moreover, compared with the multiscale diversity entropy, GCMDiEn calculates all coarse-grained time series entropy value with the same scale and extends the first-order moment to second-order moment to mitigate the shortcomings of the short time series instability and losing part of the frequency region information. This paper proposed a new rolling bearing fault diagnosis framework which combined the GCMDiEn with the empirical wavelet transform (EWT), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM). Finally, the simulation results show the superiority of the GCMDiEn method over the multiscale diversity entropy method. The proposed framework has a higher fault recognition rate (99.38%) than the existing methods.
Highlights
Rolling bearings are crucial to the automobile production line
THE PROPOSED FRAMWORK The empirical wavelet transform (EWT), generalized composite multiscale diversity entropy (GCMDiEn) and Laplacian score (LS) are described in the above sections, so we can set up a novel fault diagnosis framework of rolling bearings
The results show that five testing samples with rolling element depression (RED) were wrongly placed in rolling element wear (REW) and four testing samples with inner race fault (IRF) were wrongly placed in Norm
Summary
Rolling bearings are crucial to the automobile production line. Nowadays, the competition between the automobile industry is very fierce. Feature extraction is utilized to collect the characteristics of the time series to classify the faults. In this step, entropy based method is very efficient, which is a nonlinear dynamic method to calculate the. Unlike the most existing entropy-based feature extraction methods that focus on the static orderliness, the GCMDiEn method analyzes the dynamic complexity characteristics of the arbitrary time series by calculating the probability of the pattern similarity. A novel entropy-based feature extraction method is proposed, which analyzes the dynamic complexity characteristics of the arbitrary time series. 2. Based on the proposed entropy-based feature extraction method, a new fault diagnosis framework for rolling bearing is proposed.
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