A novel parameter-induced stochastic resonance phenomena in fractional Fourier domain

Liu, Lin; Wang, Huiqi; Lv, Wangyong; Zhong, Shun

doi:10.1016/j.ymssp.2016.02.016

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…In 2016, Wen et al classified “data-driven fault diagnosis methods” into three categories: statistical analysis methods, signal processing methods, and artificial intelligence methods [ 8 ]. The fractional Fourier transform proposed in this paper is one of the important directions in signal processing methods [ 9 , 10 , 11 ], and it was first widely used in radar, communications, information security, and other fields [ 12 , 13 , 14 ]. In recent years, the FRFT has been researched in the detection and parameter estimation of chirp signals [ 15 , 16 ], and in [ 17 ], FRFT was used for early fault diagnosis of gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Wen

2022

Sensors

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Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the safety and reliability of the motor control system. For this reason, on the basis of the powerful extraction ability of the fractional Fourier transform (FRFT) for micro fault features, especially the extraction ability to fit fractional frequency domain faults, this paper intends to establish a multilevel fine fault diagnosis method for fractional-order or integer-order faults. Firstly, this is accomplished by performing the fractional Fourier transform on the acquired data with faults and feature extraction in the multilevel fractional frequency domain and then optimizing the feature extraction model. Secondly, one further step search method is established to determine the projection direction with the largest fault feature. Thirdly, taking the extracted multilevel fault features as input, a multilevel fine fault diagnosis method based on the SVM model is established. Finally, three typical digital simulation examples and actual operating data collected by the ZHS-2 multifunctional motor test bench with a flexible rotor are employed to verify the effectiveness, robustness, and accuracy of this new method. The main contribution and innovation of this paper are that the fractional Fourier transform method based on time domain and frequency domains is introduced. This method can extract the small fault features in the maximum projection direction of the signal in the fractional domain, but detection with other time–frequency methods is difficult; the extracted multilevel fault features are used as input, and the corresponding fault diagnosis model is established, which can improve the accuracy of fault detection and ensure the safe and reliable operation of industrial equipment.

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Section: Introductionmentioning

confidence: 99%

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Wen

2022

Sensors

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“…In signal processing methods, fractional Fourier transform is one of the important directions of non-stationary signal processing, which was first widely used in radar, communication, information security, and other fields [14][15][16]. As a high-resolution and fine adaptive filtering method, FRFT makes use of the characteristics of different energy concentrations of non-stationary signals in the fractional order domain of different orders.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Wang

2022

Symmetry

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Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.

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“…Lin et al . found the phenomenon of SR in the frequency oscillation system excited by the LFM signal and Gaussian white noise [30]. However, there are few studies on the SR caused by the NLFM signal.…”

Section: Introductionmentioning

confidence: 99%

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

Yang

Zhang

et al. 2021

Phil. Trans. R. Soc. A.

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The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

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A novel parameter-induced stochastic resonance phenomena in fractional Fourier domain

Cited by 10 publications

References 37 publications

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

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