On bearing fault diagnosis by nonlinear system resonance

Zhang, Shuai; Yang, Jianhua; Zhang, Jingling; Liu, Houguang; Hu, Eryi

doi:10.1007/s11071-019-05305-x

Cited by 26 publications

(14 citation statements)

References 38 publications

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“…Lai and Leng [24] investigated the SR behavior in bistable duffing oscillator to achieve the incipient fault diagnosis. Recently, Zhang et al [25] compared the performance of undamped, underdamped and overdamped Duffing systems on weak fault feature extraction, and intuitionally revealed the superiority of undamped Duffing system. Yang et al [26] proposed the coherence resonance method in the periodic potential system, and the resonance factor index was considered as a key measurement to identify and evaluate the bearing fault.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator

Wang

Chen

Liu

2021

Meas. Sci. Technol.

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Noise-assisted stochastic resonance (SR) method has been extensively applied in bearing fault diagnosis. In order to cast off the basic constraint on nonlinearity in classical SR systems, and overturn the passive energy conversion from external noise to signal, a fluctuating-frequency linear oscillator (FFLO) is proposed and combined with the generalized scale transformation (GST) to overcome the small parameter limitation in this study. The results of output power amplification reveal that the proposed GST-FFLO system displays a rich variety of generalized SR (GSR) behaviors, which play an active role in the optimal energy conversion from internal regulatable noise to weak bearing fault signal. Moreover, it is also found that the undamped GST-FFLO system always produces more significant GSR peaks, thereby improving the precision and efficiency in the energy conversion. Finally, the experimental results demonstrate that the proposed method is always valid and exhibits the superiority in diagnosis performance and operating efficiency in several typical difficult cases, namely, impulse interference, low signal-to-noise ratio, and multiple faults.

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Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator

Wang

Chen

Liu

2021

Meas. Sci. Technol.

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“…Time frequency analysis based on vibration signal has been widely used in recent years [3,4]. However, it is difficult to extract the vibration signal of the bearing of the pumping unit in operation, and the obtained bearing fault signal is not accurate, which lead to the unsatisfactory detection result [5,6].…”

Section: Introductionmentioning

confidence: 99%

Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Zhang

2021

IEEJ Transactions Elec Engng

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In order to solve the problem that is difficult to extract the fault data of bearing of pumping unit in operation, a negative selection algorithm based on extension theory is proposed to detect the abnormal of bearing. First, a negative selection algorithm detector set model is constructed by using matter element. Synthetic correlation degree function is used as the matching rule of detector generation stage and bearing fault anomaly detection stage. Genetic Particle Swarm Optimization (GPSO) is used to generate detector set. Aiming at the problem of large redundancy of the detector set, the correlation function is used to formulate merging rules to merge the detector set interval. Finally, bearing anomaly detection is carried out by using the bearing fault data of Case Western Reserve University. The results show that the activation rate of the inner ring fault detector is 98.89%, the activation rate of the outer ring fault detector is 98.61%, and the activation rate of the ball fault detector is 99.17%.

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“…Nonetheless, MFD-FA needs refining further since suffering from some shortages [1,17]. Hence, extraction of nonlinear features for condition monitoring of rotating machinery leaves much to be desired [21].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

2021

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Defective rotating machinery usually exhibits complex dynamic behavior. Therefore, feature representation of machinery vibration signals is always critical for condition monitoring of rotating machinery. Permutation entropy (PeEn), an adaptive symbolic description, can measure complexities of signals. However, PeEn may lack the capability to fully describe dynamics of complex signals since compressing all the information into a single parameter. Afterwards, multiscale PeEn (MPeEn) is put forward for coping with nonstationarity, outliers and artifacts emerging in complex signals. In MPeEn, a set of parameters serve to describe dynamics of a complex signal in different time scales. Nonetheless, an average procedure in MPeEn may withhold local information of a complex signal. To overcome deficiencies of PeEn and MPeEn, this paper proposes generalized PeEn (GPeEn) by introducing different time lags and orders into PeEn. In GPeEn, a complex signal is compressed into one matrix rather than a single parameter. Moreover, minimal, maximal and average values of a matrix obtained by GPeEn serve to briefly describe conditions of rotating machinery. Next, a numerical experiment proves that GPeEn outperforms PeEn and MPeEn in characterizing conditions of a Lorenz model. Subsequently, the performance of GPeEn is benchmarked against that of PeEn and MPeEn by investigating gear and roll-bearing vibration signals containing different types and severity of faults. The results show that the proposed method has a clear advantage over PeEn and MPeEn in condition monitoring of rotating machinery.

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On bearing fault diagnosis by nonlinear system resonance

Cited by 26 publications

References 38 publications

Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator

Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator

Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

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