A novel mechanical fault signal feature extraction method based on unsaturated piecewise tri-stable stochastic resonance

Zhao, Shuaı; Shi, Pengfei; Han, Dongying

doi:10.1016/j.measurement.2020.108374

Cited by 47 publications

(19 citation statements)

References 38 publications

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“…At the same time, it was also confirmed in Ref. [ 27 ] that AWGN can produce the same effect, so it is meaningful for signal processing to compare the effects of the two types of noise-driving PTSR. The

and

of the periodic signal are 0.3 and 0.02, the step size h is set to 0.8, and the other parameters remain unchanged, then the signal is mixed with the two noises, respectively, and passed through the PTSR system.…”

Section: Performance Of the Ptsr System Driven By Dichotomous Noise C...mentioning

confidence: 57%

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A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

Zhao

Shi

2023

Sensors

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Stochastic resonance (SR) has been widely studied as a means of signal processing since its conception. Since SR is different from other denoising methods in nature, it can be used for not only feature extraction but also signal enhancement. Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics. In this paper, the method of piecewise tri-stable SR (PTSR) driven by dichotomous noise is studied, and it is verified that signal enhancement can still be achieved in the PTSR system. At the same time, the influence of the parameters of the PTSR system, periodic signal, and dichotomous noise on the mean of signal-to-noise ratio gain (SNR-GM) is analyzed. Finally, dichotomous noise and AWGN are used as the driving sources of the PTSR system, and the signal enhancement ability and noise resistance ability of the two drivers are compared.

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and

Section: Performance Of the Ptsr System Driven By Dichotomous Noise C...mentioning

confidence: 57%

“…In past studies, such as Ref. [ 27 ], a PTSR system was proposed, and its potential function is as follows:

where

and

are positive number and

. Figure 1 displays its potential function.…”

Section: The Ptsr System and Parametersmentioning

confidence: 99%

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

Zhao

Shi

2023

Sensors

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“…The commonly used time–frequency representation methods are classified into linear and nonlinear methods, both of which can map 1D time domain signals to 2D time-frequency planes in order to comprehensively reflect the time–frequency joint attributes of non-stationary signals [ 21 , 22 ]. Effective use of these methods can reveal the time and frequency performance of the energy contained in the rolling mill vibration signals.…”

Section: Signal Processing and Data Dimensionality Reductionmentioning

confidence: 99%

Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples

Peng

Zhang

Shi

2022

Sensors

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Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill bearing by employing the improved sparrow search algorithm deep belief network (ISAA-DBN) with limited data samples. First, the fast spectral kurtosis approach is adopted to convert the non-stationary original vibration signals collected by the acceleration sensors installed at the axial and radial ends of the rolling mill bearings into two-dimensional (2D) spectral kurtosis time–frequency images with higher feature recognition, and the principal component analysis (PCA) technique is used to decrease the dimension of the data in order to achieve a high diagnosis rate with a limited number of samples. Subsequently, the sparrow search algorithm (SSA) is used to realize the intelligent optimized self-adaptive function of a deep belief network (DBN). Furthermore, the firefly disturbance algorithm is employed to improve the spatial search capability and robustness of SSA-DBN in order to achieve better performance of the ISSA-DBN method. Finally, the proposed approach is experimentally compared to other approaches used for diagnosis. The results show that the proposed approach not only retains the useful features of the data through dimension reduction but also improves the efficiency of the diagnosis and achieves the highest diagnosis accuracy with limited data samples. In addition, the optimal position of the sensor for diagnosing rolling mill roll faults is identified.

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“…Among them, canonical correlation analysis (CCA) [2,3], principal component analysis (PCA) [4][5][6], Fisher discrimi-nant analysis (FDA) [7] and partial least squares (PLS) [8] were widely used linear feature representation methods. For nonlinear feature extraction methods, such as kernel principal component [9], kernel PLS [10], artificial neural network (ANN) [11,12] , support vector machine (SVM) [13,14] and stochastic resonance [15][16][17] are used to describe more complex data features. As a shallow learning network, the traditional feature extraction model can extract useful features in many pattern recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions

et al. 2021

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With the development of modern industries, the working environment of rotating machinery has become increasingly complicated. Therefore, it is very meaningful to accurately identify the type of equipment failure under variable operating conditions. This paper presents a rotating machinery fault diagnosis method based on dynamic learning rate deep belief network (DBN) with adaptive structure (PSO-DDBN). Firstly, the wavelet packet energy entropy principle was used to obtain the characteristic matrix of the original data, and then the characteristics of the data under variable conditions were distinguished. Secondly, in order to adjust the structure of DBN, the loss function of DBN was used to construct the convergence function in particle swarm optimization (PSO) adaptive process. The dynamic learning rate strategy was applied to the training process of the network. The network gradient value in each iteration was recorded and the dynamic learning rate function was constructed to achieve the purpose of dynamically adjusting the network learning rate and making the network convergence faster and more stable. Then, the performance of PSO-DDBN was verified by the data of bearing and gearbox under variable conditions. Finally, other intelligent diagnosis algorithms were compared with this method, and the results showed that this method had better universality and fault classification ability.

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A novel mechanical fault signal feature extraction method based on unsaturated piecewise tri-stable stochastic resonance

Cited by 47 publications

References 38 publications

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions

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