scholarly journals Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

2021 ◽  
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Health Management ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Intelligent Fault Diagnosis ◽  
Feature Extraction Method ◽  
Fault Information

Abstract Intelligent fault diagnosis provides great convenience for the prognostic and health management of the rotating machinery. Recently, the entropy-based feature extraction method has aroused researchers’ attentions due to its independence with prior knowledge, unnecessary of preprocessing, and easy to perform. The multiscale diversity entropy has been proven to be a promising feature extraction method for the intelligent fault diagnosis. Compared to the existing entropy methods, the multiscale diversity entropy has advantages of high consistency, strong robustness and high calculation efficiency. However, the multiscale diversity entropy encounters the challenge to extract features for early fault diagnosis due to the weak fault symptoms and strong noise. This can be attributed to the multiscale diversity entropy only concerns the fault information embedded in the low frequency, which ignores the information hidden in the high frequency. To address this defect, the hierarchical diversity entropy (HDE) is proposed, which can synchronously extract fault information hidden in both high and low frequency. Based on HDE and random forest, a novel intelligent fault diagnosis frame has been proposed. The effectiveness of the proposed method has been evaluated through simulated and experimental bearing signals. The results show that the proposed HDE has the best feature extraction ability compare to multiscale sample entropy, multiscale permutation entropy, multiscale fuzzy entropy, and multiscale diversity entropy.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

scholarly journals A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 212 ◽  
Author(s):  
Bin Ju ◽  
Haijiao Zhang ◽  
Yongbin Liu ◽  
Fang Liu ◽  
Siliang Lu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

scholarly journals An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE

2018 ◽  
Vol 25 (s2) ◽  
pp. 98-106 ◽  
Author(s):  
Hu Zhang ◽  
Lei Zhao ◽  
Quan Liu ◽  
Jingjing Luo ◽  
Qin Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Frequency Band ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Bearing Vibration

Abstract The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.

scholarly journals Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Maohua Xiao ◽  
Kai Wen ◽  
Cunyi Zhang ◽  
Xiao Zhao ◽  
Weihua Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction ◽  
Fault Characteristic

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

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