Fault Diagnosis of Bearing Based on KPCA and KNN Method

Wang, Qiang; Liu, Yong Bao; He, Xiuli; Liu, Shu Yong; Liu, Jian Hua

doi:10.4028/www.scientific.net/amr.986-987.1491

Cited by 19 publications

(10 citation statements)

References 3 publications

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“…k-nearest neighbour (kNN) is an effective nonparametric method for classification which is regularly used in SHM applications. 68,[89][90][91] The kNN algorithm computes the distance between each training and test sample in the dataset, returning the k closest samples. The closeness of a datapoint is determined by a distance metric such as the Euclidean distance.…”

Section: Classificationmentioning

confidence: 99%

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

Buckley

Ghosh

Pakrashi

2022

Structural Health Monitoring

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There are a large number of time domain, frequency domain and time-frequency signal processing methods available for univariate feature extraction. However, there is no consensus in SHM on which feature, or feature sets, are best suited for the identification, localisation and prognosis of damage. This paper attempts to address this problem by providing a comprehensive benchmark of feature selection & reduction methods applied to an extensive set of univariate features. These univariate features are extracted using multiple statistical, temporal and spectral methods from the benchmark S101 and Z24 bridge datasets. These datasets contain labelled accelerometer recordings from full scale bridges as they are progressively subjected to multiple damage scenarios. To identify the minimal set of features that best distinguishes between the multiple damage states, a supervised machine learning approach is used in combination with multiple feature selection methods. The ability of these reduced feature sets to distinguish between damage states is benchmarked using the prediction performance of the classification models, with the training and test sets obtained through stratified k-fold cross validation. The results obtained show that reduced sets of univariate features, extracted from a single accelerometer sensor, are capable of accurately distinguishing between multiple classes of healthy and damaged states. This work provides a benchmark for SHM practitioners and researchers alike for the choice, comparison and validation of feature extraction and feature selection methods across a wide range of systems.

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Section: Classificationmentioning

confidence: 99%

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

Buckley

Ghosh

Pakrashi

2022

Structural Health Monitoring

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“…A combination of weighted KNN (WKNN) classifiers was proposed by Y. Lei et al, [73] to overcome the two previously mentioned disadvantages of KNN-based REB fault detection and diagnosis. The KNN was also combined with other classification methods to enhance the REB fault detection and diagnosis capability, such as with SVM [74], kernel PCA (KPCA) [75], the fuzzy C-means method [76], the binary differential evolution algorithm [77], or the K-star classifier [78]. More recently, an optimal KNN model was combined with KPCA to deal with bearing fault detection and diagnosis, in which the KNN was optimized using a particle swarm optimization method [79].…”

Section: K-nearest Neighbor (Knn)-based Reb Phmmentioning

confidence: 99%

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

et al. 2019

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The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.

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“…Among the various pattern recognition methods, the machine learning-based method is the most used. Wang et al [11] used KPCA to extract features from bearing fault signal and used k-nearest neighbor (KNN) as a classifier to achieve diagnosis; Fei et al [12] reconstructed the characteristics of bearing vibration signal after singular value decomposition based on wavelet packet transform phase space and established support vector machine (SVM) model of bearing diagnosis; Mahamad and Hiyama [13] performed fast Fourier transform (FFT) and envelope processing on the bearing vibration signal, extracted time domain and frequency domain feature as input, and then used ANN to fulfill the diagnosis. However, the existing intelligent fault diagnosis methods based on the above feature extraction and classification still have three limitations: first, the feature extraction methods often require the operators to have professional prior knowledge and rich experience.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

Wang

Huang

Dai

et al. 2020

Shock and Vibration

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This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the learned features in the network training process. Moreover, applying this network in fault diagnosis of bearing date provided by the Case Western Reserve University (CWRU) Bearing Data Center, it was found that the convolution kernel could also achieve the above operation. The novel network of this paper achieved a good classification effect on both the simulated signals and the measured signals.

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Fault Diagnosis of Bearing Based on KPCA and KNN Method

Cited by 19 publications

References 3 publications

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

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