A novel sensor fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition and Probabilistic Neural Network

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

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A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Entropy ◽

10.3390/e21070680 ◽

2019 ◽

Vol 21 (7) ◽

pp. 680 ◽

Cited By ~ 4

Author(s):

Zhang ◽

Zhou

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Fault Classification ◽

Rolling Bearings ◽

Feature Vectors ◽

Mode Decomposition ◽

Diagnosis Method ◽

Candidate Feature

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

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A fault diagnosis method combined with ensemble empirical mode decomposition, base-scale entropy and clustering by fast search algorithm for roller bearings

Journal of Vibroengineering ◽

10.21595/jve.2016.17221 ◽

2016 ◽

Vol 18 (7) ◽

pp. 4472-4490 ◽

Cited By ~ 4

Author(s):

Rong Zhang ◽

Fan Xu ◽

Yan Jun Fang ◽

Zheng Min Kong ◽

Ruo Li Tang

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Search Algorithm ◽

Ensemble Empirical Mode Decomposition ◽

Fast Search ◽

Roller Bearings ◽

Fast Search Algorithm ◽

Mode Decomposition ◽

Diagnosis Method

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Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

Mathematical Problems in Engineering ◽

10.1155/2020/8882653 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jiakai Ding ◽

Dongming Xiao ◽

Liangpei Huang ◽

Xuejun Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Hopfield Neural Network ◽

Sample Entropy ◽

Feature Vectors ◽

Mode Decomposition ◽

Gear Fault ◽

Diagnosis Method ◽

Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

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