scholarly journals Extraction method of composite fault features of gear transmission system based on demodulation of NMD and Teager energy operators

2021 ◽  
Vol 54 (1-2) ◽  
pp. 129-140
Author(s):  
Jingyue Wang ◽  
Yuefu Liu ◽  
Haotian Wang ◽  
Jiaqiang E
Keyword(s):  
Comparative Analysis ◽  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
Energy Operator ◽  
Transmission System ◽  
Gear Transmission ◽  
Teager Energy Operator ◽  
Gear Transmission System ◽  
Simulation Signal ◽  
Teager Energy

In order to effectively identify and extract the composite fault characteristics of the gear transmission system, a composite fault diagnosis method combining nonlinear mode decomposition (NMD) and Teager energy operator demodulation is proposed. Because the envelope demodulation of Hilbert transform has the disadvantages of large amount of calculation and end effect, it uses Teager energy operator to solve the problem of large amount of calculation, and NMD solves the problem that the fault signal features is not easy to extract under the mode aliasing. First, the NMD method is used to decompose the fault simulation signal, and the nonlinear modal component with practical physical significance is obtained. Secondly, the Teager energy operator demodulation is carried out for the nonlinear modal components, and the demodulation results are analyzed to verify the feasibility of the method. Then, the method is applied to the composite fault diagnosis of gear pitting wear in gear transmission system, and the characteristic frequency obtained from the test data is compared with the calculated characteristic frequency. The comparative analysis shows that the method can separate the fault characteristic frequencies of large and small gears. The comparative analysis with EMD and EEMD methods in simulation signal analysis and experimental research shows that this method is superior.

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

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.

De-Noising and Analyzing for Non-Stationary Signal Based on MATLAB

2014 ◽  
Vol 889-890 ◽  
pp. 795-798
Author(s):  
Xian Cheng ◽  
Wei Min Dong
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Simulation Experiment ◽  
Fault Simulation ◽  
Vibration Signal ◽  
Transmission System ◽  
Gear Transmission ◽  
Gear Transmission System ◽  
Gear Mechanism ◽  
Non Stationary Signal

This paper studies the application of wavelet analysis in the fault diagnosis of mechanical system which describes the principle of wavelet analysis and its application in fault diagnosis of gear mechanism. It can identify and eliminate the failure by analyzing the vibration signal obtained from the fault simulation experiment of gear transmission system. Wavelet analysis in MATLAB can extract some important fault characteristics that the other methods cannot extract them. Application of wavelet analysis in the fault diagnosis of gear transmission system is effective by pretreating the characteristic information that extracted from gear transmission system.

scholarly journals A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Teager Energy Operator ◽  
Rolling Element ◽  
Element Bearing ◽  
Teager Energy

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.

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