Bearing fault diagnosis through stochastic resonance by full-wave signal construction with half-cycle delay

Chi, Kuo; Kang, Jianshe; Bajrić, Rusmir; Zhang, Xinghui

doi:10.1016/j.measurement.2019.106893

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…In 2019, Kuo Chi et al [3], worked on SR to have the ability to improve the weak fault-induced impulses in envelope signal. Nevertheless, the envelope signal was a unipolar half-wave signal that was poorer than the bipolar full-wave signal for the SR effect.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In dynamic conditions, a synchronized phasor measurement unit was exploited to calculate the fault position in [13] exploiting two-terminal data. The majority significant requirement of these methods [3][4][5][6][7][8][9][10][11][12][13] was that they need fault classification, i.e., fault type to be recognized initial to start the fault position methods. But that, these methods were exploited for un-compensated lines that might not work accurately for series compensated lines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Deep Convolutional Neural Network for Fault Signal Recognition in the Power Distribution System

C¹,

S²,

Subrahmanyam³

2019

JCMPS

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Currently Electrical power system is represented as the complex artificial system throughout the world, as the economic and social advancements based upon consistent, intact, stable and economic models. Because of varied arbitrary causes, unintentional failures happen in electrical power systems. Based on this problem, this work aims to develop EDCNN to detect and classify the fault signals like sag transient and swell in the transmission line. Moreover, the wavelet decomposed fault signals are extracted and identify the fault on the basis of the decomposed signal using the EDCNN approach. Moreover, this paper presents the performance evaluation by deciding the Type I and Type II measures and RMSE measures. In the performance analysis, it evaluates the performance of EDCNN method to several existing methods. The experimentation outcomes examine that the proposed EDCNN method efficiently and classifies recognizes the fault signal in the power distribution system while comparing with the conventional methods such as linear-Support Vector Machine (SVM), quadratic-SVM, and Radial Basis Function (RBF)-NN and Gradient Descent (GD)-NN models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Convolutional Neural Network for Fault Signal Recognition in the Power Distribution System

C¹,

S²,

Subrahmanyam³

2019

JCMPS

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“…According to the application type, there are two main directions of technical diagnostics: monitoring and diagnostics of rotating machinery, [1][2][3][4] and structural health monitoring (SHM), which is defined as a process of implementing a damage detection and characterisation strategy for various engineering structures. [5][6][7][8] According to the used technique, diagnostic methods can be divided into: destructive technique when maximum load or fatigue test of samples are performed 9,10 ; and non-destructive technique when the state of the structure is evaluated without causing damage to it. 11,12 Due to this advantage, the nondestructive technique has become the most common technique for technical diagnostics nowadays.…”

Section: Introductionmentioning

confidence: 99%

“…For centrifugal compressor and other non-contact rotating machinery, the noise is easily added to interfere the detection signal, thus reducing the effect of fault feature recognition with traditional noise reduction analysis and signal decomposition methods. [7][8][9] However, noise can be reasonably used by stochastic resonance (SR) to obtain fault features. Therefore, there are many feature recognition methods using SR and bistable stochastic resonance (BSR) for centrifugal compressor and other non-contact rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Song

et al. 2022

Structural Health Monitoring

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The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.

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“…[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] This type of signal detection method extracts the characteristics of weak signals, not by eliminating or suppressing noise, but by using the characteristics of some nonlinear systems to detect weak signals in strong noise. In the early weak fault detection methods of rolling bearings, the stochastic resonance [26][27][28] and the chaotic oscillator 8 are widely used. The parameter sensitivity of the chaotic oscillator and the immunity to noise make it have certain advantages in detecting weak fault signals with lower signal-to-noise ratio.…”

mentioning

confidence: 99%

Periodic‐phase‐diagram similarity method for weak signal detection

Tian

Zhang

Yang

et al. 2021

Int Journal of Mech Sys Dyn

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The periodic‐phase‐diagram similarity method is proposed to identify the frequency of weak harmonic signals. The key technology is to find a set of optimal coefficients for Duffing system, which leads to the periodic motion under the influence of weak signal and strong noise. Introducing the phase diagram similarity, the influences of strong noise on the similarity of periodic phase diagram are discussed. The principle of highest similarity of periodic phase diagram with the same frequency is detected by discussing the persistence of similarity of periodic motion phase diagram under the strong noise and the periodic‐phase‐diagram similarity method is constructed. The weak signals of early fault and strong noise are input into Duffing system to obtain the identified system. The stochastic subharmonic Melnikov method is extended to obtain the conditions of the optimal coefficients for the identified system. Based on the results, the constructed frequency conversion harmonic weak signals are considered to form a datum periodic system. With the change of frequency in the datum periodic system, the phase diagram similarity of the two constructed systems can be calculated. Based on the periodic‐phase‐diagram similarity method, the frequency of weak harmonic signals can be identified by the principle of highest similarity of periodic phase diagram with the same frequency. The results of numerical simulation and the early fault diagnosis results of actual bearings verify the feasibility of the periodic‐phase‐diagram similarity method. The accuracy of the detection effect is 97%, and the minimum signal‐to‐noise ratio is −80.71 dB.

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Bearing fault diagnosis through stochastic resonance by full-wave signal construction with half-cycle delay

Cited by 14 publications

References 26 publications

Enhanced Deep Convolutional Neural Network for Fault Signal Recognition in the Power Distribution System

Enhanced Deep Convolutional Neural Network for Fault Signal Recognition in the Power Distribution System

Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance

Periodic‐phase‐diagram similarity method for weak signal detection

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