Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

Wang, Zhenya; Yao, Ligang; Chen, Gang; Ding, Jiaxin

doi:10.1016/j.isatra.2020.12.054

Cited by 124 publications

(45 citation statements)

References 37 publications

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“…As such, the nonlinear separable problem can be transformed into a linear separable problem. Therefore, the SVM algorithm performs well with nonlinear data classification problems and it is widely used in the fault diagnosis of rotating machinery equipment [ 35 , 36 , 37 ], but the problem of parameter selection must be addressed. According to the above analyses, for solving the difficulties experienced with SVM classification model application, we selected the sparrow search algorithm (SSA) to search for the optimal SVM parameters ( c , σ ) and set the SVM classification error rate as the fitness function; the flow chart and mathematical model are shown in Figure 3 and Formula (10), respectively.…”

Section: Ssa-svm Algorithmmentioning

confidence: 99%

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

Zhang

Wang

2021

Entropy

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The working environment of wind turbine gearboxes is complex, complicating the effective monitoring of their running state. In this paper, a new gearbox fault diagnosis method based on improved variational mode decomposition (IVMD), combined with time-shift multi-scale sample entropy (TSMSE) and a sparrow search algorithm-based support vector machine (SSA-SVM), is proposed. Firstly, a novel algorithm, IVMD, is presented for solving the problem where VMD parameters (K and α) need to be selected in advance, which mainly contains two steps: the maximum kurtosis index is employed to preliminarily determine a series of local optimal decomposition parameters (K and α), then from the local parameters, the global optimum parameters are selected based on the minimum energy loss coefficient (ELC). After decomposition by IVMD, the raw signal is divided into K intrinsic mode functions (IMFs), the optimal IMF(s) with abundant fault information is (are) chosen based on the minimum envelopment entropy criterion. Secondly, the time-shift technique is introduced to information entropy, the time-shift multi-scale sample entropy algorithm is applied for the analysis of the complexity of the chosen optimal IMF and extract fault feature vectors. Finally, the sparrow search algorithm, which takes the classification error rate of SVM as the fitness function, is used to adaptively optimize the SVM parameters. Next, the extracted TSMSEs are input into the SSA-SVM model as the feature vector to identify the gear signal types under different conditions. The simulation and experimental results confirm that the proposed method is feasible and superior in gearbox fault diagnosis when compared with other methods.

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Section: Ssa-svm Algorithmmentioning

confidence: 99%

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

Zhang

Wang

2021

Entropy

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“…For example, in the wavelet transform method, the suitable wavelet bases should be pre-set, meaning that it lacks self-adaptability. On the other hand, empirical mode decomposition is a type of adaptive time-frequency analysis algorithm [13]; Ensemble Empirical Mode Decomposition (EEMD) [14] can overcome the modal aliasing effect of EMD effectively [15]. However, EEMD still has two issues that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…Schoen et al used Fast Fourier Transform (FFT) to extract the most pertinent information for motor fault diagnosis, and the results show that they can accurately extract fault features [19]. Wang et al proposed a novel intelligent fault-diagnosis method based on generalised composite multiscale weighted permutation entropy (GCMWPE), and this method was able to correctly diagnose bearing faults [13]. Khelil et al used ANN to monitor engine health and diagnose faults.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Yan

Jiang

2022

Polish Maritime Research

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The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

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“…Compared with MPE, WMPE reduces the standard deviation, which ensures the results are more robust. [ 8 , 9 ]. Combining the advantages of MWPE, the literature quantifies the non−linear characteristics of bearing vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy

Yuan

Fan

Zhou

et al. 2022

Entropy

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Due to the complicated engineering operation of the check valve in a high−pressure diaphragm pump, its vibration signal tends to show non−stationary and non−linear characteristics. These leads to difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi−scale weighted permutation entropy (MWPE) is based on extracting multi−scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single−scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non−parallel classification hyperplanes in the equipment state space to improve the model’s applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%.

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Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

Cited by 124 publications

References 37 publications

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy

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