A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis

Li, Jimeng; Zhang, Yungang; Xie, Ping

doi:10.1016/j.measurement.2016.05.086

Cited by 48 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, genetic algorithm(GA) and particle swarm optimization(PSO) are widely used in fault feature extraction. Li, et al [51], presented an adaptive cascaded stochastic resonance method to detect the weak impulsive features submerged in noise; the multi-parameters of this method were optimized by GA synchronously, and results showed that the proposed method was suitable for extracting the weak impact features of a gearbox. Lu, et al [52], applied GA to search the optimal multi-wavelets from an adaptive multi-wavelet library.…”

Section: Fault Feature Extraction Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

show abstract

Section: Fault Feature Extraction Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…The SNRs from each subfigure imply that the SVVR method is more effective in extracting the weak feature component than the MSTSR method. A higher SNR indicates a more accurate detection performance for fault signals [31][32][33][34].…”

Section: Simulation Signal Analysismentioning

confidence: 99%

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Liu

Dai

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

Bearing is a key part of rotary machines, and its working condition is critical in normal operation of rotary machines. Vibrational signals are usually analyzed to monitor the status of bearing. However, information on the status of bearing is always buried in heavy background noise; that is, status information of bearing is weaker than the background noise. Extracting the status features of bearing from signals buried in noise is difficult. Given this, a step-varying vibrational resonance (SVVR) method based on Duffing oscillator nonlinear system is proposed to enhance the weak status feature of bearing by tuning different parameters. Extraction ability of SVVR was verified by analyzing simulation signal and practical bearing signal. Experimental results show that SVVR is more effective in extracting weak characteristic information than other methods, including multiscale noise tuning stochastic resonance (SR), Woods-Saxon potential-based SR, and joint Woods-Saxon and Gaussian potential-based SR. Two evaluation indices are investigated to qualitatively and quantitatively assess the fault detection capability of the SVVR method. The results show that the SVVR can effectively identify the weak status information of bearing.

show abstract

“…Over the past decade, numerous fault diagnosis techniques such as expert systems, fuzzy logic, machine learning algorithm, and model-based methods have been introduced for rotating machinery fault diagnosis. [2][3][4][5] Fault diagnosis is described as the process to determine whether the system is operating normally or not and to find incipient failures and their causes based on operational information obtained from the mechanical equipment. 6,7 Fault feature extraction which can be accomplished through a vibration signal process is the key step of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Improved ensemble local mean decomposition based on cubic trigonometric cardinal spline interpolation and its application for rotating machinery fault diagnosis

Chen

et al. 2020

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Ensemble local mean decomposition has been gradually introduced into mechanical vibration signal processing due to its excellent performance in electroencephalogram signal analysis. However, an unsatisfactory problem is that ensemble local mean decomposition cannot effectively process vibration signals of complex mechanical system due to the constraints of moving average. The process of moving average is time-consuming and inaccurate in complex signal analysis. Therefore, an improved ensemble local mean decomposition method called C-ELMD with modified envelope algorithm based on cubic trigonometric cardinal spline interpolation is proposed in this article. First, the shortcomings in sifting process of ensemble local mean decomposition is discussed and, furthermore, advantages and disadvantages of the common interpolation methods adopted to improve ensemble local mean decomposition are compared. Then, cubic trigonometric cardinal spline interpolation is employed to construct the local mean and envelope curves in a more precise way. In addition, the influence of shape-controlling parameter on envelope estimation accuracy in cubic trigonometric cardinal spline interpolation is also discussed in detail to select an optimal shape-controlling parameter. The effectiveness of cubic trigonometric cardinal spline interpolation for improving the accuracy of ensemble local mean decomposition is demonstrated using a synthetic signal. Finally, the proposed cubic trigonometric cardinal spline interpolation is tested to be effective in gear and bearing fault detection and diagnosis.

show abstract

A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis

Cited by 48 publications

References 24 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Improved ensemble local mean decomposition based on cubic trigonometric cardinal spline interpolation and its application for rotating machinery fault diagnosis

Contact Info

Product

Resources

About