Adaptive feature extraction for low speed rolling bearing fault diagnosis based on improved Teager energy operator, Auto-encoder and PCA

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

Download Full-text

Bearing Fault Diagnosis Based on CEEMDAN and Teager Energy Operator

Journal of Physics Conference Series ◽

10.1088/1742-6596/1345/3/032044 ◽

2019 ◽

Vol 1345 ◽

pp. 032044 ◽

Cited By ~ 1

Author(s):

Xiao Jing ◽

Mei Jianmin ◽

Zhang Zhiqiang ◽

Chang Chun

Keyword(s):

Fault Diagnosis ◽

Energy Operator ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Teager Energy Operator ◽

Teager Energy

Download Full-text

Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder

Measurement ◽

10.1016/j.measurement.2021.109452 ◽

2021 ◽

pp. 109452

Author(s):

Xinglong Pei ◽

Xiaoyang Zheng ◽

Jinliang Wu

Keyword(s):

Fault Diagnosis ◽

Compressed Sensing ◽

Energy Operator ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Teager Energy Operator ◽

Teager Energy

Download Full-text

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.

Download Full-text

Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

Shock and Vibration ◽

10.1155/2019/3742512 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Longlong Li ◽

Yahui Cui ◽

Runlin Chen ◽

Lingping Chen ◽

Lihua Wang

Keyword(s):

Fault Diagnosis ◽

Singular Value Decomposition ◽

Energy Operator ◽

Vibration Signal ◽

Rolling Bearing ◽

Singular Value ◽

Teager Energy Operator ◽

Singular Component ◽

Value Decomposition ◽

Teager Energy

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD-AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD-AKTEO. Simulation data and real data from a run-to-failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

Download Full-text

Research on rolling bearing fault diagnosis based on multi-dimensional feature extraction and evidence fusion theory

Royal Society Open Science ◽

10.1098/rsos.181488 ◽

2019 ◽

Vol 6 (2) ◽

pp. 181488 ◽

Cited By ~ 4

Author(s):

Jingchao Li ◽

Yulong Ying ◽

Yuan Ren ◽

Siyu Xu ◽

Dongyuan Bi ◽

...

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Fault Diagnosis ◽

Real Time ◽

Rolling Bearing ◽

Diagnostic Methods ◽

Bearing Fault ◽

Feature Vectors ◽

Bearing Fault Diagnosis ◽

Fusion Theory

Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, a multi-dimensional feature extraction strategy on the basis of entropy characteristics, Holder coefficient characteristics and improved generalized box-counting dimension characteristics is executed for extracting health status feature vectors from vibration signals. And, secondly, a grey relation algorithm is used to calculate the basic belief assignments (BBAs) using the extracted feature vectors, and lastly, the BBAs are fused through the Yager algorithm for achieving bearing fault pattern recognition. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities in comparison with the existing intelligent diagnostic methods based on a small number of training samples with good real-time performance, and may be used for online assessment.

Download Full-text

Feature Extraction of Rolling Bearing Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.190-191.993 ◽

2012 ◽

Vol 190-191 ◽

pp. 993-997

Author(s):

Li Jie Sun ◽

Li Zhang ◽

Yong Bo Yang ◽

Da Bo Zhang ◽

Li Chun Wu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Attribute Reduction ◽

Vibration Signal ◽

Rolling Bearing ◽

Mechanical Equipment ◽

Characteristic Parameters ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

Download Full-text