Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings

Zeng, Ming; Yu, Yang; Zheng, Jinde; Cheng, Junsheng

doi:10.1016/j.ymssp.2015.06.006

Cited by 41 publications

(25 citation statements)

References 27 publications

(38 reference statements)

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“…e influence of the threshold will be described in the later section. So according to the table, Symptom Parameters 14,13,4,11,1,7,8,12,18,9,10, and 17 are dominant symptom parameters. Other Symptom Parameters 6, 3, 5, 16, 2, and 15 are labelled as insensitive parameters and should be removed from fault diagnosis data sets.…”

Section: Evaluation Criterion To Select the Dominant Symptommentioning

confidence: 99%

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

Zuo

Liao

2020

Mathematical Problems in Engineering

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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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Section: Evaluation Criterion To Select the Dominant Symptommentioning

confidence: 99%

“…ey cover from the time domain to the frequency domain. Equations (1)∼ (18) show different symptom parameters [18].…”

Section: Fault Diagnosis Symptom Parametersmentioning

confidence: 99%

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

Zuo

Liao

2020

Mathematical Problems in Engineering

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“…Inspired by the basic theory of SVM and flexible convex hull, MMC-FCH method was proposed by Zeng [20,21]. e main idea of MMC-FCH is replacing the convex hull in SVM with the flexible convex hull, while the optimized hyperplane is obtained by the same approach as SVM.…”

Section: Mmc-fchmentioning

confidence: 99%

“…In short, ASNBD is inspired by the basic theory of EMD and MP and proposed by avoiding their disadvantages and learning their advantages. e maximum margin classification based on flexible convex hulls (MMC-FCH), which was inspired by SVM, was proposed by Zeng [20,21]. e class regions of the sample sets are approximated by convex hulls of the training samples in SVM.…”

Section: Introductionmentioning

confidence: 99%

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Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow‐Band Decomposition and MMC‐FCH

Cheng

Chen

Liu

et al. 2019

Shock and Vibration

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Adaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. The optimized objective function is a regulated singular local linear operator so that each obtained component is limited to be a LNB signal. Afterward, a component is generated by filtering the original signal with the optimized filter. Compared with MP, ASNBD is superior in both the physical meaning and the adaptivity. Drawbacks in EMD such as end effect and mode mixing are reduced in the proposed method because the application of interpolation function is not required. To achieve the fault diagnosis of roller bearings, raw signals are decomposed by ASNBD at first. Then, appropriate features of the decomposed results are chosen by applying distance evaluation technique (DET). Afterward, different faults are recognized by utilizing maximum margin classification based on flexible convex hulls (MMC-FCH). Comparisons between EMD and ASNBD show that the proposed method performs better in the antinoise performance, accuracy, orthogonality, and extracting the fault features of roller bearings.

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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Cheng

Liu

et al. 2017

Front. Mech. Eng.

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Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings

Cited by 41 publications

References 27 publications

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

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

Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow‐Band Decomposition and MMC‐FCH

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

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