Research on Fault Diagnosis Theory and Key Technology for Power Machinery

2013 ◽  
Vol 427-429 ◽  
pp. 312-315
Author(s):  
Hong Zhang ◽  
Shou Sheng Zhang ◽  
Jun Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Experimental Analysis ◽  
Singular Value ◽  
Rotor System ◽  
Smoothing Method ◽  
End Effect ◽  
Key Technology ◽  
Power Machinery ◽  
Value Decomposition

Based on the disadvantages of the original LMD algorithm in terms of smoothing method and end effect, this paper puts forward to a method of diagnosis of rotor system faults based on the improved LMD and singular value decomposition. The PF component obtained after applying the improved LMD method is able to separate the aliasing modes while maintaining the natures and features of the original PF component. Thus, the systems real information can be grasped more effectively. According to the experimental analysis, this method can be applied to diagnosis of rotor system faults effectively.

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.

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