Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214550014 ◽

2014 ◽

Vol 229 (10) ◽

pp. 1887-1903 ◽

Cited By ~ 11

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.

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Permutation entropy-based 2D feature extraction for bearing fault diagnosis

Nonlinear Dynamics ◽

10.1007/s11071-020-06014-6 ◽

2020 ◽

Vol 102 (3) ◽

pp. 1717-1731

Author(s):

Mantas Landauskas ◽

Maosen Cao ◽

Minvydas Ragulskis

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Permutation Entropy ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Bearing Fault Identification Based on Deep Convolution Residual Network

Mechanika ◽

10.5755/j02.mech.28265 ◽

2021 ◽

Vol 27 (3) ◽

pp. 229-236

Author(s):

Tong ZHOU ◽

Yuan LI ◽

Yijia JING ◽

Yifei TONG

Keyword(s):

Feature Extraction ◽

Deep Neural Networks ◽

High Accuracy ◽

Normal Operation ◽

Fault Identification ◽

Mechanical Equipment ◽

Residual Network ◽

Bearing Fault ◽

Fitting Ability ◽

Very High

Bearings are important parts in industrial production and are related to the normal operation of mechanical equipment. For bearing fault identification， traditional method often includes feature extraction, which involves professional prior knowledge and is time-consuming. This paper proposes the deep convolution residual network (DCRN) to identify the bearing fault. Based on the end-to-end learning characteristics of deep neural networks, this method can directly use raw data for training, and does not require feature extraction. Moreover, under the effect of skip connection, DCRN can exert the powerful fitting ability of deep neural network. In this paper, by stacking residual blocks, three different architecture of DCRN are designed and all three achieve very high accuracy, respectively 99.60%, 99.71% and 99.81%. Compared with other methods, DCRN have better generalization performance.

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A Feature Extraction Method Based on VMD and RDT and Its Application in Engine Crankshaft Bearing Fault

10.20944/preprints201803.0266.v1 ◽

2018 ◽

Author(s):

Gang Ren ◽

Jide Jia ◽

Jianmin Mei ◽

Xiangyu Jia ◽

Jiajia Han

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Random Noise ◽

Permutation Entropy ◽

Noise Robustness ◽

Feature Extraction Method ◽

Bearing Fault ◽

Random Decrement Technique ◽

Mode Decomposition ◽

Random Decrement

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

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A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

Entropy ◽

10.3390/e23020191 ◽

2021 ◽

Vol 23 (2) ◽

pp. 191

Author(s):

Yi Zhang ◽

Yong Lv ◽

Mao Ge

Keyword(s):

Neural Network ◽

Time Series ◽

Bp Neural Network ◽

Rolling Bearing ◽

Fault Classification ◽

Permutation Entropy ◽

Fault Identification ◽

Depth Information ◽

Bearing Fault ◽

Iterative Filtering

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

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