Journal ArticleDOI
A Sound-Based Fault Diagnosis Method for Railway Point Machines Based on Two-Stage Feature Selection Strategy and Ensemble Classifier
TLDR
In this paper , a sound-based diagnosis method for railway point machines (RPMs) is presented, where the sound signals are preprocessed using empirical mode decomposition (EMD) and the first 15 intrinsic mode functions (IMFs) are extracted.Abstract:
Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method. read more
Citations
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Journal ArticleDOI
Two‐stage gradient‐based iterative algorithms for the fractional‐order nonlinear systems by using the hierarchical identification principle
TL;DR: This article focuses on the parameter estimation issues for a fractional‐order nonlinear system with autoregressive noise and proposes a two‐stage moving‐data‐window gradient‐based iterative algorithm to reduce the complexity and improve the identification accuracy.
Journal ArticleDOI
Parameter-varying artificial potential field control of virtual coupling system with nonlinear dynamics
TL;DR: Wang et al. as discussed by the authors proposed a virtual coupling scheme based on local leader-follower method to achieve high precision control of a virtual coupled train with nonlinear dynamics, and the results showed that the variable parameter artificial potential field controller with changing the weight can reduce the average error of the stop by 0.4304 compared with the traditional controller.
Journal ArticleDOI
Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data
TL;DR: The results of the least squares and multi-innovation least squares algorithms for linear regressive systems with white noises can be extended to other systems with colored noises as mentioned in this paper , and the results of least square and multinomial least square algorithms can be generalized to other problems with different noises.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
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Book ChapterDOI
A Practical Approach to Feature Selection
Kenji Kira,Larry A. Rendell +1 more
TL;DR: Comparison with other feature selection algorithms shows Relief's advantages in terms of learning time and the accuracy of the learned concept, suggesting Relief's practicality.
Journal ArticleDOI
A novel SVM-kNN-PSO ensemble method for intrusion detection system
TL;DR: A novel ensemble construction method that uses PSO generated weights to create ensemble of classifiers with better accuracy for intrusion detection and results suggest that the new approach can generate ensembles that outperform WMA in terms of classification accuracy.
Journal ArticleDOI
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
TL;DR: Experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity, so the approach has possibility for bearing incipient fault diagnosis.