Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection

Tang, Xianghong; Wang, Jiachen; Lu, Jianguang; Liu, Guokai; Chen, Jiadui

doi:10.3390/app8112143

Cited by 20 publications

(11 citation statements)

References 23 publications

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“…In this section, we first improves the FF-FC-MIC feature selection algorithm in the literature [31] and proposes a new feature selection method, FSFN (feature selection feedback network), for the feature dimension and feature selection time feedback function, which can perform online fault diagnosis feature selection. Then, a multi-sensor information fusion method is proposed based on the kappa coefficient to improve the accuracy and reliability of bearing fault diagnosis.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…First, we set the feature quantity threshold and feature selection time threshold. Then, we use the FF-FC-MIC algorithm proposed by [31] to select the feature for which the selected feature dimension and feature time are compared with the set two thresholds. When the feedback condition is satisfied, the weight of each feature in the feature set is calculated by CART, and features with a weight of zero are eliminated.…”

Section: A Feature Selection Feedback Network(fsfn)mentioning

confidence: 99%

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A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Tang

Wang

et al. 2020

IEEE Access

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Bearings running state affects the normal operation of mechanical equipment. It is of great theoretical and practical value to carry out bearing fault diagnosis. In bearing fault diagnosis research, the extraction and selection of fault features can help improving the accuracy of bearing fault diagnosis. However, these researches suffer from the following weaknesses. (1) High dimension of the selected features.(2) Uncertainty of single sensor for data sampling. Therefore, in this paper, a feature selection feedback network (FSFN) is proposed to overcome the first weakness. At the same time, we proposed an improved Dempster-Shafer (IDS) evidence theory fusion method based on the kappa coefficient to deal with the second weakness. Extensive evaluations of the proposed method on the CUT-2 experimental platform dataset showed that FSFN can not only reduce the dimension of the final selected feature without decreasing the diagnostic accuracy but also shorten the time of feature selection. Moreover, compared with the existing DS evidence theory fusion method, IDS can achieve higher average fusion precision and improve the accuracy and reliability of bearing fault diagnosis.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: A Feature Selection Feedback Network(fsfn)mentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Tang

Wang

et al. 2020

IEEE Access

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“…Feature Extraction Classification Accuracy (%) [30] HOSA + PCA "one-against all" SVM 96.98 [31] Time-frequency domain ANN 93.00 [32] Time-and frequency-domains SVM 98.70 [33] IMFs decomposed by EEMD SVM with parameter optimized by ICD 97.91 [34] EEMD-MPE SSDAE 99.60 [35] CNNEPDNN CNNEPDNN 98.10 [36] FF_FC_MIC SVM 99.17 [37] HHT-WMSC SVM…”

Section: Referencementioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

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Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

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“…The optimal feature selection method should not only reduce data dimensions, but also eliminate redundant and irrelevant features. Therefore, considering correlations in feature selection plays a crucial role in reducing data dimensions [29]. However, in the construction of feature subsets, only relying on a single correlation or sensitivity measurement method will bias the calculation results to some extent, which will reduce the robustness of the feature subsets.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

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Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection

Cited by 20 publications

References 23 publications

A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

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