Multi-scale stochastic resonance spectrogram for rolling element bearing defect diagnosis

2016 ◽  
Author(s):  
Qingbo He ◽  
Enhao Wu
Keyword(s):  
Stochastic Resonance ◽  
Rolling Element Bearing ◽  
Multi Scale ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Defect Diagnosis

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

2019 ◽  
Vol 41 (14) ◽  
pp. 4013-4022 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor

2013 ◽  
Vol 588 ◽  
pp. 333-342 ◽  
Author(s):  
Leon Swędrowski ◽  
Kazimierz Duzinkiewicz ◽  
Michał Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Neural Networks ◽  
Induction Motor ◽  
Measurement Data ◽  
Failure Detection ◽  
Rolling Element Bearing ◽  
Bearing Defect ◽  
Instantaneous Current ◽  
Rolling Element ◽  
Current Change ◽  
Element Bearing

Bearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect transformation, namely Clark transformation. It determines the vector of the spatial stator current based on instantaneous current measurements of the induction motor supply phases current. The analysis of the processed measurement data used multilayered, one-directional neural networks, which are particularly attractive due to their nonlinear structure and ability to learn. During the research 40 bearings: undamaged, with damages of three types and various degrees of fault extent, were used. The conducted research proves the efficiency of neural networks for detection and recognition of faults in induction motor bearings. In case of tests of the unknown state bearings, an efficiency approach to failure detection equaled 77%.

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