Bearing Fault Diagnosis Using Weighted K-Nearest Neighbor

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

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Distance and Density Similarity Based Enhancedk-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Shock and Vibration ◽

10.1155/2016/3843192 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Sharif Uddin ◽

Md. Rashedul Islam ◽

Sheraz Ali Khan ◽

Jaeyoung Kim ◽

Jong-Myon Kim ◽

...

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Similarity Measure ◽

Classification Accuracy ◽

Diagnostic Performance ◽

Nearest Neighbor ◽

Neighborhood Size ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

An enhancedk-nearest neighbor (k-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditionalk-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size,k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposedk-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhancedk-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size,k.

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Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Mathematical Problems in Engineering ◽

10.1155/2020/2495068 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Shilun Zuo ◽

Zhiqiang Liao

Keyword(s):

Fault Diagnosis ◽

Discriminant Analysis ◽

Nearest Neighbor ◽

Canonical Discriminant Analysis ◽

Selection Scheme ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Parameters Selection ◽

Fault Features ◽

Diagnosis Model

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

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Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2016/1205868 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Li Jiang ◽

Shunsheng Guo

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Nearest Neighbor ◽

Feature Space ◽

High Dimensional ◽

Dimensional Manifold ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Low Dimensional ◽

Low Dimensional Manifold

The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. Due to its outstanding performance in enhancing the intraclass compactness and interclass dispersibility, MKMFA is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples. A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults. It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. Subsequently, the sensitive low-dimensional characteristics in feature space are inputted into K-nearest neighbor classifier so as to distinguish various fault patterns. The four-fault-type and ten-fault-severity bearing fault diagnosis experiment results show the feasibility and superiority of the proposed scheme in comparison with the other five methods.

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Improved multi-bandwidth mode manifold for enhanced bearing fault diagnosis

10.21203/rs.3.rs-19146/v1 ◽

2020 ◽

Author(s):

Guifu Du ◽

Tao Jiang ◽

Jun Wang ◽

Xingxing Jiang ◽

Zhongkui Zhu

Keyword(s):

Fault Diagnosis ◽

Nearest Neighbor ◽

Simulation Analysis ◽

Learning Algorithm ◽

Dimensional Manifold ◽

The Real ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Low Dimensional

Abstract Variational mode decomposition (VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold (Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment (LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the performance of the TM method. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD (RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor (Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method proposed over the traditional methods.

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Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x17693492 ◽

2017 ◽

Vol 231 (2) ◽

pp. 200-206 ◽

Cited By ~ 11

Author(s):

Xueli An ◽

Luoping Pan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Nearest Neighbor ◽

Roller Bearing ◽

Permutation Entropy ◽

Variational Mode Decomposition ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition

Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.

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Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Shock and Vibration ◽

10.1155/2018/6714520 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Zhe Tong ◽

Wei Li ◽

Bo Zhang ◽

Meng Zhang

Keyword(s):

Fault Diagnosis ◽

Working Conditions ◽

Nearest Neighbor ◽

Domain Adaptation ◽

Vibration Signal ◽

Feature Representation ◽

Training Data ◽

Fast Fourier Transformation ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

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