scholarly journals THE SUPPORT VECTOR MACHINE PARAMETER OPTIMIZATION METHOD BASED ON ADAPTIVE ELITIST DIFFERENTIAL EVOLUTION ALGORITHM AND ITS APPLICATION TO ROLLER BEARING FAULT DIAGNOSIS

2022 ◽  
Vol 36 (06) ◽  
Author(s):  
HUNGLINH AO ◽  
THANHHANG NGUYEN ◽  
V.HO HUU ◽  
TRANGTHAO NGUYEN
Keyword(s):  
Singular Value Decomposition ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Roller Bearing ◽  
Optimization Method ◽  
Singular Value ◽  
Superior Performance ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Value Decomposition

SVM parameters have serious effects on the accuracy rate of classification result. Tuning SVM parameters is always a challenge for scientists. In this paper, a SVM parameter optimization method based on Adaptive Elitist Differential Evolution (AeDE-SVM) is proposed. Furthermore, AeDE-SVM is applied to diagnose roller bearing fault by using complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) techniques. First, original acceleration vibration signals are decomposed into Intrinsic Mode Function (IMFs) by using CEEMD method. Second, initial feature matrices are extracted from (IMFs) by singular value decomposition (SVD) techniques to obtain single values. Third, these values serve as input vector for AeDE-SVM classifier. The results show that the combination of AeDE-SVM classifiers and the CEEMD-SVD method obtains higher classification accuracy and lower cost time compared to other methods. In this paper, the roller bearing vibration signals were used to evaluate the proposed method. The experimental results showed that the superior performance compared to other SVM parameter optimization techniques and successfully recognized different fault types of roller bearing during its operation.

Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Guangming Dong ◽  
Jin Chen ◽  
Fagang Zhao
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Machine Learning Techniques ◽  
Minimum Entropy ◽  
Bearing Fault ◽  
Machinery Condition Monitoring ◽  
Learning Techniques ◽  
Fault Features ◽  
Incipient Fault ◽  
Value Decomposition

Machinery condition monitoring and fault diagnosis are essential for early detection of equipment malfunctions or failures, which insure productivity, quality, and safety in the manufacturing process. This paper aims at extracting fault features of rolling element bearings at the incipient fault stage. K-singular value decomposition (K-SVD), one technique for sparse representation of signals, is used for study. In K-SVD, its dictionary is trained from data by machine learning techniques, which allows more flexibility to adapt to variation of real signals than the predefined dictionaries. Analysis on simulated bearing signals and real signals shows that K-SVD can give better bearing fault features than the predefined dictionaries such as wavelet dictionaries. However, during our simulation study, K-SVD was found to have large representation error under heavy noise. To reduce the noise effect, minimum entropy deconvolution (MED) is used as a prefilter. The combination of MED and K-SVD is proposed for incipient bearing fault detection. The method is verified by simulation and experimental study. It is shown that the proposed method can effectively extract the impulsive fault feature of the tested bearing at its incipient fault stage.

scholarly journals Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiqiang Liao ◽  
Xuewei Song ◽  
Baozhu Jia ◽  
Peng Chen
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Extraction Method ◽  
Singular Values ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Fault Features ◽  
Value Decomposition ◽  
Fault Feature Extraction

Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.

Bearing Fault Parameter Identification Under Varying Operating Conditions Using Vibration Signals and Evolutionary Algorithms

2014 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Roller Bearing ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
System Response ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Fault Parameter

This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.

scholarly journals An Optimization Method for Hyperspectral Endmember Extraction Based on K-SVD

2019 ◽  
Vol 85 (12) ◽  
pp. 879-887
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Ya Zhang ◽  
Yun Tang
Keyword(s):  
Singular Value Decomposition ◽  
Background Noise ◽  
Optimization Method ◽  
Extraction Methods ◽  
Singular Value ◽  
Endmember Extraction ◽  
Partially Observed ◽  
Mixed Pixels ◽  
Ground Object ◽  
Value Decomposition

Mixed pixels are common in hyperspectral imagery (<small>HSI</small>). Due to the complexity of the ground object distribution, some end-member extraction methods cannot obtain good results and the processes are complex. Therefore, this paper proposes an optimization method for <small>HSI</small> endmember extraction, which improves the accuracy of the results based on K-singular value decomposition (<small>K-SVD</small>). The proposed method comprises three core steps. (1) Based on the contribution value of initial endmembers, partially observed data selected according to the appropriate confidence participate in the calculation. (2) Construction of the error model to eliminate the background noise. (3) Using the <small>K-SVD</small> to perform column-by-column iteration on the endmembers to achieve the overall optimality. Experiments with three real images are applied, demonstrating the proposed method can improve the overall endmember accuracy by 15.1%–55.7% compared with the original methods.

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