Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.

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An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app8122352 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2352 ◽

Cited By ~ 16

Author(s):

Yonggang Xu ◽

Kun Zhang ◽

Chaoyong Ma ◽

Xiaoqing Li ◽

Jianyu Zhang

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Vibration Acceleration ◽

Rolling Bearings ◽

Bearing Fault ◽

Acceleration Signal ◽

Trend Component ◽

Empirical Wavelet Transform ◽

The Fourier Transform

As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

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An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

IEEE Access ◽

10.1109/access.2019.2902645 ◽

2019 ◽

Vol 7 ◽

pp. 30437-30456 ◽

Cited By ~ 6

Author(s):

Yonggang Xu ◽

Kun Zhang ◽

Chaoyong Ma ◽

Zhipeng Sheng ◽

Hongchen Shen

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Segmentation Method ◽

Rolling Bearings ◽

Empirical Wavelet Transform

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Frequency Phase Space Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

IEEE Access ◽

10.1109/access.2019.2922248 ◽

2019 ◽

Vol 7 ◽

pp. 86306-86318 ◽

Cited By ~ 5

Author(s):

Xin Huang ◽

Guangrui Wen ◽

Lin Liang ◽

Zhifen Zhang ◽

Yuan Tan

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Phase Space ◽

Rolling Bearings ◽

Empirical Wavelet Transform ◽

Frequency Phase

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Enhanced Empirical Wavelet Transform with Application to Rolling Bearings Fault Diagnosis

Structural Health Monitoring 2017 ◽

10.12783/shm2017/13890 ◽

2017 ◽

Cited By ~ 1

Author(s):

XIAOTONG TU ◽

YUE HU ◽

FUCAI LI

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearings ◽

Empirical Wavelet Transform

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Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.268 ◽

2011 ◽

Vol 121-126 ◽

pp. 268-272 ◽

Cited By ~ 2

Author(s):

Ke Li ◽

Yue Lei Zhang ◽

Zhi Xiong Li

Keyword(s):

Kalman Filter ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Rolling Bearing ◽

Support Vector ◽

Measured Signal ◽

Fault Features ◽

Bearing Condition Monitoring ◽

Filtering Technique

In the condition monitoring and fault diagnosis, useful information about the incipient fault features in the measured signal is always corrupted by noise. Fortunately, the Kalman filtering technique can filter the noise effectively, and the impending system fault can be revealed to prevent the system from malfunction. This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis. A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented. The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

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Sparsity-guided multi-scale empirical wavelet transform and its application in fault diagnosis of rolling bearings

Journal of the Brazilian Society of Mechanical Sciences and Engineering ◽

10.1007/s40430-021-03117-y ◽

2021 ◽

Vol 43 (8) ◽

Author(s):

Kun Zhang ◽

Weikang Tian ◽

Peng Chen ◽

Chaoyong Ma ◽

Yonggang Xu

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearings ◽

Multi Scale ◽

Empirical Wavelet Transform

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Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising

Measurement ◽

10.1016/j.measurement.2019.107392 ◽

2020 ◽

Vol 152 ◽

pp. 107392 ◽

Cited By ~ 10

Author(s):

Jimeng Li ◽

Hui Wang ◽

Xiangdong Wang ◽

Yungang Zhang

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Empirical Wavelet Transform

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Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform

Shock and Vibration ◽

10.1155/2020/3218564 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Yangli Ou ◽

Shuilong He ◽

Chaofan Hu ◽

Jiading Bao ◽

Wenjie Li

Keyword(s):

Genetic Algorithms ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Total Variation ◽

Adaptive Signal Processing ◽

Economic Losses ◽

Particle Swarm Algorithm ◽

Rolling Bearings ◽

Noise Interference ◽

Empirical Wavelet Transform

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

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