A SVDD and <i>K</i>‐Means Based Early Warning Method for Dual‐Rotor Equipment under Time‐Varying Operating Conditions

Jiang, Zhinong; Hu, Minghui; Feng, Kun; Hao, Wang

doi:10.1155/2018/5382398

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…Its functional reliability has an extraordinary effect on the stability and safety of dual-rotor equipment. Even a small incipient defect may produce chain reaction and further lead to heavy casualties [1]. Intelligent fault diagnosis serves an essential role in pursuing the relationship between the monitoring data and the health states of bearings to prevent unpredictable failure in dual-rotor equipment [2][3].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

Feng

et al. 2020

IEEE Access

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Machine learning methods have made great development in data-driven fault diagnosis of rolling bearings. But the intelligent fault diagnosis of intershaft bearing faces the following two dilemmas: 1) the fault vibration is extremely weak, and it is difficult to extract features that can distinguish different classes; 2) due to the complex and variable working condition, the intershaft bearing does not always fail in same working conditions. That is, the labeled training data is not sufficient for every source domain. These challenges lead to the failure of traditional machine learning based fault diagnosis for intershaft bearings. Therefore, a novel intelligent fault diagnosis scheme is investigated for intershaft bearings of dual-rotor equipment under variable working conditions. The paper focuses on two key issues: 1) developing a feature extraction approach with which the fault features with excellent clustering and separation are extracted from vibration signals. This approach addresses the dilemma of weak fault feature extraction of intershaft bearing and creates a feasible precondition for subsequent feature transfer; 2) proposing a feature transfer method transforming the labeled sample features in multiple source domains into the trainable sample features in the target domain. This new transfer achieves the sharing of labeled training samples under working conditions and enriches the trainable samples in target domain. Ultimately, the faults of intershaft bearings can be diagnosed with the help of the neural network classifier trained by the transferred samples with labels. Experimental results verify that this established scheme is effective and superior to other comparable methods for the transfer diagnosis task from multiple source domains to target domain.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

Feng

et al. 2020

IEEE Access

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“…There are some studies covering different SVDD algorithm applications [15][16][17]-most of them optimizing the hyper-parameters using approaches like grid search, which is computationally expensive. To obtain these parameters in a more efficient manner, some authors have considered metaheuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

et al. 2020

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

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Fault detection based on squirrel search algorithm and support vector data description for industrial processes

2022

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A SVDD and K‐Means Based Early Warning Method for Dual‐Rotor Equipment under Time‐Varying Operating Conditions

Cited by 10 publications

References 33 publications

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

Fault detection based on squirrel search algorithm and support vector data description for industrial processes

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