Gear fault identification based on Hilbert–Huang transform and SOM neural network

Cheng, Gang; Cheng, Yulong; Shen, Lihua; Qiu, Jingjun; Zhang, Shuai

doi:10.1016/j.measurement.2012.10.026

Cited by 98 publications

(61 citation statements)

References 17 publications

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“…The decomposed process of EMD is as follows [13][14][15]: When all h 1 ðtÞ satisfy two definitions of IMF, the first IMF c 1 ðtÞ is obtained. The remaining part of the original signal r 1 ðtÞ is defined as a new signal to repeat EMD decomposition process until the resulting signal is below a pre-given value.…”

Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

et al. 2015

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Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

et al. 2015

Self Cite

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“…The sifting process is completed until the resulting signal is monotonous or lower than the pre-specified value (Cheng, et al, 2013). Thus the analytical signal consists with n-empirical modes and a residue is achieved as:…”

Section: Empirical Mode Decomposition and Support Vector Machines 21mentioning

confidence: 99%

Diagnosis for gear tooth surface damage by empirical mode decomposition in cyclic fatigue test

Fan

Ikejo

Nagamura

et al. 2014

JAMDSM

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Gear is one of the most important and commonly used components in machine system. Some gear failure may lead to fatal damage of the entire system, or even huge losses in industrial production. Early detection of gear damage is crucial to prevent the machine system from malfunction. This paper provides an intelligent diagnosis method for gear damage based on techniques of empirical mode decomposition and support vector machines. By the data processing of empirical mode decomposition, the original signal are decomposed into a finite set of intrinsic mode functions with frequency bands ranging from high to low. The characteristic energy ratios of intrinsic mode functions are acquired as representative parameters of the signal. Furthermore, statistical parameters of standard deviation, root mean square value, kurtosis and skewness are extracted from the original signal. Characteristic energy ratios and statistical parameters are combined as failure feature vectors to be input to the support vector machines classifiers for gear damage diagnosis. The validity of the presented method is confirmed by the application of monitoring gear conditions during the cyclic fatigue test. The vibration accelerations of gear box are acquired to illustrate the progression of pitting damage. Most of the gear conditions are identified, indicating the effectiveness of the proposed method.

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“…With the development of information processing technology, considerable research attention has been devoted to design and analysis of FE scheme by using computer‐based learning techniques including neural network‐based methods and iterative learning schemes. However, the former methods are time‐consuming for online training of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The reference [27] investigates the fault detection and accommodation problem for a class of nonlinear time delay systems with uncertainties and external disturbance. Among these FE schemes designed for nonlinear time delay systems, tracking error and system state error in previous iteration have not been considered in the current iteration.With the development of information processing technology, considerable research attention has been devoted to design and analysis of FE scheme by using computer-based learning techniques [28][29][30] including neural network-based methods and iterative learning schemes. However, the former methods are time-consuming for online training of neural networks.…”

mentioning

confidence: 99%

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

Feng

Chai

et al. 2017

Intl J Robust & Nonlinear

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Summary This paper deals with the intermittent fault estimation problem for a class of nonlinear time‐delay systems with measurement noise. The time delays are assumed to occur in state vector, nonlinear term as well as output vector, thus reflecting the time delays influence in reality more closely. The aim of the problem is to estimate the intermittent fault by using iterative learning scheme, with the property of H∞ index, hence attenuating the influence from measurement noise. Different from existing fault estimating schemes, the state error information and fault estimating information in the previous iteration are used in the current iteration to improve the estimating results. The stability and convergence of iterative learning observer and uniform boundedness of dynamic error system are achieved by using Lyapunov function and optimal function design. Simultaneously, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality by the Schur complements and Young relations. Furthermore, the results are both suited for the systems with time‐varying delay and the systems with constant delay. Finally, two numerical examples are proposed to illustrate the effectiveness of the proposed method, and a comparability example is presented to demonstrate its superiority. Copyright © 2017 John Wiley & Sons, Ltd.

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Gear fault identification based on Hilbert–Huang transform and SOM neural network

Cited by 98 publications

References 17 publications

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Diagnosis for gear tooth surface damage by empirical mode decomposition in cyclic fatigue test

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

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