Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

Lu, Junjie; Huang, Jinquan; Lü, Feng

doi:10.3390/en10010039

Cited by 42 publications

(25 citation statements)

References 30 publications

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“…Due to the slow convergence and a large number of parameters set shortcomings, the diagnosis accuracy of BP classifier is low, and thus its practical application is limited [26]. As support vector machines need to determine the kernel function, the large-scale classification training time will be longer [27]. Therefore, it is necessary to develop the new classifiers to identify the fault characteristics and to determine the fault type of rolling bearing.…”

Section: Classifier Based On the Kernel Density Estimationmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Ge¹,

Wang²,

Ren³

2017

Entropy

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This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM) classifier and back-propagation (BP) neural network classifier.

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Section: Classifier Based On the Kernel Density Estimationmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Ge¹,

Wang²,

Ren³

2017

Entropy

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“…ELM is a learning algorithm that covers the Single Layer Feed Forward Neural Network (SLFN) structure and it has an adequate performance without any necessity of iterative process [34]. Since it was first proposed, ELM has been applied to classification and regression models in the various field of research as computer vision, biomedical signal processing and so on [35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Event Detection Using a Fast Extreme Learning Machine

et al. 2018

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Abstract:Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.

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“…Riahi et al distinguished signals of different corrosion stages with a BP neural network in acoustic emission testing of a tank bottom [26,27]. However, a BP neural network has the disadvantages of complex parameter setting, slow convergence, falling easily into local minima, and limited accuracy and scope of application [28]. Compared to a BP neural network, SVM generalization performance is better, but it still requires manual assignment of kernel functions and kernel function parameters [29,30], which limits the significance of SVM applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers

Xing-meng

2017

Entropy

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According to non-stationary characteristic of the acoustic emission signal of rolling element bearings, a novel fault diagnosis method based on empirical wavelet transform (EWT) and ambiguity correlation classification (ACC) is proposed. In the proposed method, the acoustic emission signal acquired from a one-channel sensor is firstly decomposed using the EWT method, and then the mutual information of decomposed components and the original signal is computed and used to extract the noiseless component in order to obtain the reconstructed signal. Afterwards, the ambiguity correlation classifier, which has the advantages of ambiguity functions in the processing of the non-stationary signal, and the combining of correlation coefficients, is applied. Finally, multiple datasets of reconstructed signals for different operative conditions are fed to the ambiguity correlation classifier for training and testing. The proposed method was verified by experiments, and experimental results have shown that the proposed method can effectively diagnose three different operative conditions of rolling element bearings with higher detection rates than support vector machine and back-propagation (BP) neural network algorithms.

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Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

Cited by 42 publications

References 30 publications

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Power Quality Event Detection Using a Fast Extreme Learning Machine

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers

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