Research on feature enhancement method of weak fault signal of rotating machinery based on adaptive stochastic resonance

Gao, Kangping; Xu, Xinxin; Li, Jiabo; Jiao, Shengjie; Shi, Ning

doi:10.1007/s12206-022-0104-z

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…e monostable stochastic resonance model is taken as the research object in this paper. e nonlinear equation can be expressed by the Langevin equation [23][24][25]:…”

Section: Weak Signal Processing Methods For Time Grid Sensormentioning

confidence: 99%

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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Because the output signal of the time grid sensor is so weak and is easily affected by the disturbing noise, it is difficult to extract the induction signal containing the position information, when the time grid sensor is used to detect the rotor position of the permanent magnet synchronous motor (PMSM). In this paper, an adaptive monostable stochastic resonance method is proposed to realize the weak signal detection of the time grid sensor. The tunnel magnetoresistance effect sensor (TMR) is adopted to detect the change of the rotor magnetic field of the motor in this method, and the detection model of the time grid sensor about PMSM is constructed. The monostable stochastic resonance theory is applied to extract the induction signal of the time grid sensor. As to the problem of the tuning the parameters of the monostable stochastic resonance system, the genetic algorithm is adopted to optimize the parameters of the monostable stochastic resonance system. The experimental results show that the monostable stochastic resonance method based on the genetic algorithm can effectively detect the weak induction signal of the time grid sensor about PMSM. This method solves the problem of the weak signal extraction of the time grid sensor for PMSM, and the detection accuracy is also improved.

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“…e monostable stochastic resonance model is taken as the research object in this paper. e nonlinear equation can be expressed by the Langevin equation [23][24][25]:…”

Section: Weak Signal Processing Methods For Time Grid Sensormentioning

confidence: 99%

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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“…Basing on analysis of Figure 6, it can be known that: (1) The change of m and s has a great impact on MPE curve, moreover, the degree of influence on diverse signals' MPE curves are significantly different. (2) Compared with other values, the MPE curve is an outlier when m equals 3. (3) With the deepening of fault severity, the MPE curves of different m become more distributed.…”

Section: Influence Of Parameter Variation On Multi-scale Permutation ...mentioning

confidence: 98%

Analysis of hydraulic system’s weak fault based on variable parameter multi-scale permutation entropy and deep belief network

Huang

Fang

Cheng

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at automatic recognition of hydraulic system’s weak fault, an analysis method based on variable parameter multi-scale permutation entropy (VPMPE) and deep belief network (DBN) is proposed in this paper. The external vibration signals of experimental hydraulic equipment in normal state and three different leakage state (slight, moderate and severe) are taken as research object. By the proposed method, experiment signals are first processed to obtain their multi-scale permutation entropy in different conditions by changing the embedding dimension m and the scale factor s, then the multi-scale permutation entropy under different m and s are combined to form feature vectors, and lastly the DBN classifier is used to identify and analyze the testing samples. Verification test shows that the proposed method has good effects on hydraulic system’s weak fault, which can accurately judge whether there is leakage fault and measure the fault severity.

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“…The quasi-static response is mainly concentrated in the low-frequency section of the overall dynamic response [35]. It is assumed that the discrete signal 𝑥(𝑡) obtained after continuous signal 𝑥(𝑛) acquisition can be in the form of a series of harmonic superposition, and the expression is shown in (3):…”

Section: Quasi-static Component Of Deflection Response and Its Extrac...mentioning

confidence: 99%

Study of the Reasonable Acquisition Frequency Evaluation Method of Midspan Deflection of Medium- and Small-Span Beam Bridges

Tan,

Wu,

Tang

2023

Applied Sciences

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The objective of this study is to further promote and apply the structural monitoring system to medium and small bridges, given the relative delays in scientific research, technical specifications, and engineering practice for the safety monitoring of small and medium-span girder bridges, as well as the relative simplicity of the structural system of these bridges, their well-defined forces, and the relatively large proportion of live load responses during operation. These concepts are proposed based on the evaluation method of live loading (As therefore, this paper suggests the notion of sensor acquisition frequency and appropriate acquisition frequency based on the live load assessment method and the fundamental reliability theory). Based on the time-history curve depicting the midspan deflection response of the vehicle-bridge coupling system, the frequency domain analysis reveals that the power spectrum at −3 dB corresponds to the response cutoff frequency. Significantly, the cutoff frequency mentioned is double the acquisition frequency considered suitable for the study. Based on the definition of a quasi-static response, it can be deduced that the velocity of a load does not exert any influence on the quasi-static response of a bridge structure. As a result, the derivation of the components related to the midspan deflection of a bridge’s quasi-static response is presented, together with a recommended set of methodological guidelines for the extraction of finite elements. This study introduces a novel approach for determining the cutoff frequency of the structural response by utilizing the characteristics of amplitude spectrum estimation and power spectrum estimation in frequency domain analysis. The cutoff frequency of the signal is determined by analyzing the amplitude-frequency curve of the power spectrum. Subsequently, the probability density function of the original time-history curve data is estimated based on the amplitude spectrum. Finally, reliability analysis is conducted by calculating the ratio of the amplitude spectrum area of the signal obtained through a reasonable acquisition frequency to the area of the amplitude spectrum function of the original signal. This analysis verifies the reliability of the proposed method for determining the midspan deflection acquisition frequency.

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Research on feature enhancement method of weak fault signal of rotating machinery based on adaptive stochastic resonance

Cited by 6 publications

References 29 publications

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Analysis of hydraulic system’s weak fault based on variable parameter multi-scale permutation entropy and deep belief network

Study of the Reasonable Acquisition Frequency Evaluation Method of Midspan Deflection of Medium- and Small-Span Beam Bridges

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