Bearing fault diagnosis with an improved high frequency resonance technique

2012 ◽  
Author(s):  
Mawuena Segla ◽  
Shaoping Wang ◽  
Fang Wang
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Resonance Technique ◽  
Frequency Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
High Frequency Resonance

Energy Operator Demodulation Method and Its Application in Gears Fault Diagnosis

2011 ◽  
Vol 403-408 ◽  
pp. 2972-2980 ◽  
Author(s):  
Chun Wang ◽  
Dong Ling Peng ◽  
Ge Zhu
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Engineering Application ◽  
Energy Operator ◽  
Frequency Resolution ◽  
Resonance Technique ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
High Frequency Resonance ◽  
Demodulation Method

Base on the analysis of the localization of the demodulation methods, a new optimizing arithmetic model, the energy operator optimizing arithmetic, is put forward first. Through theoretical analysis and engineering application, this new arithmetic is compared with the classic arithmetic, Hilbert demodulating method, from the precision, sensitivity, anti-jamming, complexion and some other fields by experiments. At last, this dissertation validates that the high-frequency resonance technique based on energy operator optimizing arithmetic which combine with LabView is availability with gear fault diagnosis that increase the frequency resolution of demodulation spectrum.

scholarly journals Improved Empirical Wavelet Transform for Compound Weak Bearing Fault Diagnosis with Acoustic Signals

2020 ◽  
Vol 10 (2) ◽  
pp. 682 ◽  
Author(s):  
Chaoren Qin ◽  
Dongdong Wang ◽  
Zhi Xu ◽  
Gang Tang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Frequency Modulation ◽  
Acoustic Signal ◽  
High Frequency ◽  
Acoustic Signals ◽  
Bearing Fault ◽  
Low Snr ◽  
Bearing Fault Diagnosis ◽  
Empirical Wavelet Transform

Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases. Thus, a small number of non-contact microphone sensors are a suboptimal choice, but it will result in some problems, e.g., underdetermined compound fault detection from a low signal-to-noise ratio (SNR) acoustic signal. Empirical wavelet transform (EWT) is a signal processing algorithm that has a dimension-increasing characteristic, and is beneficial for solving the underdetermined problem with few microphone sensors. However, there remain some critical problems to be solved for EWT, especially the determination of signal mode numbers, high-frequency modulation and boundary detection. To solve these problems, this paper proposes an improved empirical wavelet transform strategy for compound weak bearing fault diagnosis with acoustic signals. First, a novel envelope demodulation-based EWT (DEWT) is developed to overcome the high frequency modulation, based on which a source number estimation method with singular value decomposition (SVD) is then presented for the extraction of the correct boundary from a low SNR acoustic signal. Finally, the new fault diagnosis scheme that utilizes DEWT and SVD is compared with traditional methods, and the advantages of the proposed method in weak bearing compound fault diagnosis with a single-channel, low SNR, variable speed acoustic signal, are verified.

scholarly journals A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 707
Author(s):  
Kehan Chen ◽  
Yuting Lu ◽  
Lifeng Lin ◽  
Huiqi Wang
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Linear Oscillator ◽  
Scale Transformation ◽  
Periodic Signal ◽  
High Frequency Signal ◽  
Nonlinear Dynamical ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document