NDLI: Bearing-fault diagnosis using non-local means algorithm and empirical mode decomposition-based feature extraction and two-stage feature selection

Content Provider	IET Digital Library
Author	Van, Mien Kang, Hee Jun
Abstract	Bearing-fault-diagnosis problem can be conceived as a pattern recognition problem, which includes three main phases: feature extraction, feature selection and feature classification. Thus, to improve the performance of the whole bearing-fault-diagnosis system, the performance of each phase must be improved. The aim of this study is threefold. First, in the feature extraction step, a new feature extraction technique based on non-local-means de-noising and empirical mode decomposition is developed to more accurately obtain fault-characteristic information. Second, in the feature selection phase, a novel two-stage feature selection, hybrid distance evaluation technique (DET)–particle swarm optimisation (PSO), is proposed by combining DET and PSO to select the superior combining feature subset that discriminates well among classes. Third, in the classification phase, a comparison among three types of popular classifiers: K-nearest neighbours, probabilistic neural network and support-vector machine is done to figure out the sensitivity of each classifier corresponding to the irrelevant and redundant features and the curse of dimensionality; then, find out a most suitable classifier incorporating with feature selection phase. The experimental results for the vibration signal of the bearing are shown to verify the effectiveness of the proposed fault-diagnosis scheme.
Starting Page	671
Ending Page	680
Page Count	10
ISSN	17518822
Volume Number	9
e-ISSN	17518830
Issue Number	Issue 6, Sep (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/9/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2014.0228
Journal	IET Science, Measurement & Technology
Publisher Date	2015-04-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bearing-fault-diagnosis Problem Civil And Mechanical Engineering Computing Decomposition DET Digital Signal Processing Distance Evaluation Technique Empirical Mode Decomposition Fault Diagnosis Fault-characteristic Information Feature Classification Feature Extraction Feature Extraction Technique Feature Selection K-nearest Neighbour Knowledge Engineering Technique Machine Bearings Mechanical Component Mechanical Engineering Application of IT Mechanical Engineering Computing Neural Computing Technique Neural Nets Nonlocal-means Denoising Algorithm Optimisation Optimisation Technique Particle Swarm Optimisation Pattern Recognition Problem Probabilistic Neural Network Probability PSO Signal Classification Signal Processing And Detection Statistics Support Vector Machine Support-vector Machine Two-stage Feature Selection Vibration Signal
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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