scholarly journals A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

2019 ◽  
Vol 21 (3) ◽  
pp. 654-664 ◽  
Author(s):  
Peiming Shi ◽  
Xiaojie Ma ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Compressed Sensing ◽  
Stochastic Resonance ◽  
Rotating Machinery ◽  
Diagnosis Method ◽  
Weak Fault

scholarly journals A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

2018 ◽  
Vol 20 (1) ◽  
pp. 258-271 ◽  
Author(s):  
Dongying Han ◽  
Shujun An ◽  
Peiming Shi ◽  
Ying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Weak Fault

Research on the Time-Frequency Analysis Method to Extract Early Fault Features of Rotating Machinery

2012 ◽  
Vol 190-191 ◽  
pp. 1371-1375
Author(s):  
Ping Hua Ju ◽  
Gen Bao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Mechanical Equipment ◽  
Analysis Method ◽  
Time Frequency ◽  
Fault Features ◽  
Common Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault ◽  
Basic Features

Early fault features of rotating machinery is very weak and is disturbed by strong noise generally. how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. An intensive study is given to basic features of rotating machinery early faults and common diagnosis method, And also summarized the research status of early diagnosis in the field of mechanical equipment signal feature extraction and fault diagnosis, analyzed the current problems, and finally briefly pointed out the development of early fault diagnosis in machinery applications.

Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

2019 ◽  
Vol 10 (01) ◽  
pp. 1941003 ◽  
Author(s):  
Kuo Chi ◽  
Jianshe Kang ◽  
Xinghui Zhang ◽  
Fei Zhao
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fractional Order ◽  
Signal To Noise Ratio ◽  
Economic Losses ◽  
Integration Step ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault

Bearing is among the most widely used components in rotating machinery. Its failure can cause serious economic losses or even disasters. However, the fault-induced impulses are weak especially for the early failure. As to the bearing fault diagnosis, a novel bearing diagnosis method based on scale-varying fractional-order stochastic resonance (SFrSR) is proposed. Signal-to-noise ratio of the SFrSR output is regarded as the criterion for evaluating the stochastic resonance (SR) output. In the proposed method, by selecting the proper parameters (integration step [Formula: see text], amplitude gain [Formula: see text] and fractional-order [Formula: see text]) of SFrSR, the weak fault-induced impulses, the noise and the potential can be matched with each other. An optimal fractional-order dynamic system can be generated. To verify the proposed SFrSR, numerical tests and application verification are conducted in comparison with the traditional scale-varying first-order SR (SFiSR). The results prove that the parameters [Formula: see text] and [Formula: see text] affect the SFrSR effect seriously and the proposed SFrSR can enhance the weak signal while suppressing the noise. The SFrSR is more effective for bearing fault diagnosis than SFiSR.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Stochastic Resonance with a Bistable Confining Potential

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi
Keyword(s):  
Fault Diagnosis ◽  
Barrier Height ◽  
Stochastic Resonance ◽  
Potential Model ◽  
Rotating Machinery ◽  
Potential Well ◽  
Confining Potential ◽  
Bistable Potential ◽  
Weak Fault ◽  
Sr Method

Since the weak fault characteristics of mechanical equipment are often difficult to extract in strong background noise, stochastic resonance (SR) is widely used to extract the weak fault characteristics, which is able to utilize the noise to amplify weak fault characteristics. Although classical bistable stochastic resonance (CBSR) can enhance the weak characteristics by adjusting the parameters of potential model, when potential barrier height is adjusted potential well width is also changed and vice versa. The simultaneous change of both potential well width and barrier height is difficult to obtain a suitable potential model for better weak fault characteristic extraction and further fault diagnosis of machinery. For this reason, the output signal-to-noise ratio (SNR) of CBSR is greatly reduced, and the corresponding enhancement ability of weak fault characteristics is limited. In order to avoid the shortcomings, a new SR method is proposed to extract weak fault characteristics and further diagnose the faults of rotating machinery, where the classical bistable potential is replaced with a bistable confining potential to get the optimal SR. The bistable confining potential model not only has the characteristics of the classical bistable potential model but also has the ability to adjust the potential width, barrier height, and wall steepness independently. Simulated data are used to demonstrate the proposed new SR method. The results indicate that the weak fault characteristics can be effectively extracted from simulated signals with heavy noise. Experiments on the bearings and planetary gearboxes demonstrate that the proposed SR method can correctly diagnose the faults of rotating machinery and moreover has higher spectrum peak and better recognition degree compared with the CBSR method.

A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity

2021 ◽  
pp. 107413
Author(s):  
Dawei Gao ◽  
Yongsheng Zhu ◽  
Zhijun Ren ◽  
Ke Yan ◽  
Wei Kang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Weak Fault

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

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