Bearing fault identification by higher order energy operator fusion: A non-resonance based approach

2016 ◽  
Vol 381 ◽  
pp. 83-100 ◽  
Author(s):  
H. Faghidi ◽  
M. Liang
Keyword(s):  
Energy Operator ◽  
Higher Order ◽  
Fault Identification ◽  
Bearing Fault ◽  
Order Energy

scholarly journals Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1560 ◽  
Author(s):  
Tomasz Ciszewski ◽  
Len Gelman ◽  
Andrew Ball
Keyword(s):  
Data Processing ◽  
Rolling Bearing ◽  
Higher Order ◽  
Spectral Triple ◽  
Fault Identification ◽  
Electromechanical Systems ◽  
Processing Technologies ◽  
Bearing Fault ◽  
Triple Correlation ◽  
Electrical Data

It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

Detection of Bearing Fault Detection From Heavily Contaminated Signals: A Higher-Order Analytic Energy Operator Method

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
H. Faghidi ◽  
M. Liang
Keyword(s):  
High Frequency ◽  
Alternative Energy ◽  
Operator Method ◽  
Energy Operator ◽  
Higher Order ◽  
Energy Transformation ◽  
Bearing Fault ◽  
Frequency Modulations ◽  
High Frequency Resonance ◽  
Experimental Comparisons

This paper reports a higher-order analytic energy operator (HO-AEO) approach to monitoring bearing health conditions from vibrations signals that are polluted by strong noise and multiple interferences. The proposed analytic energy operator (AEO) is formed using the raw signal, its Hilbert transform, and their derivatives. In analogy to the conventional energy operator (EO), it represents an alternative energy transformation. However, unlike the conventional EO, it exploits the information from both the real and imaginary parts of the analytic signal. It can also extract both the amplitude and frequency modulations and is thus well suited for detecting impulsive fault signature. The joint use of multiple higher-order AEOs can further offset noise effect. The built-in amplitude demodulation (AD) capability of the proposed HO-AEO eliminates the enveloping step required by most high-frequency resonance (HFR) methods. The method is simple and easy to implement. Our simulation and experimental results have demonstrated that the proposed method can effectively extract bearing fault signature in the presence of heavy noise and multiple vibration interferences. It has also been shown mathematically that the HO-AEO processed signal yields higher signal-to-interference ratio (SIR) than the conventional EO does. The simulation and experimental comparisons also indicate that the proposed method has much better noise and interference handling capabilities than the conventional EO.

scholarly journals Higher-order energy consistent time integrators for nonlinear thermoviscoelastodynamics

PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 4070007-4070008
Author(s):  
Michael Groß ◽  
Peter Betsch
Keyword(s):  
Higher Order ◽  
Order Energy ◽  
Time Integrators

scholarly journals Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer

2020 ◽  
Vol 10 (17) ◽  
pp. 5827 ◽  
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Hybrid Procedure ◽  
Second Step ◽  
Fault Identification ◽  
Support Vector ◽  
Bearing Fault ◽  
Adaptive Technique ◽  
Fuzzy Like ◽  
Signal Approximation

In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively.

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