Unified discriminant manifold learning for rotating machinery fault diagnosis

Yang, Changyuan; Ma, Sai; Han, Qinkai

doi:10.1007/s10845-022-02011-1

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Once a fault occurs in rotating machinery, it will cause huge economic loss and security threats. Therefore, scholars have presented many methods to detect its faults and ensure its safe operation [1]. Wang et al [2] proposed a new index named symbolic sample entropy to measure the complexity of fault information, then it was combined with an enhanced analytic hierarchy process to indentify the faults of rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rotating Machinery Based on Improved CBR

Xie

Yang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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A novel method to detect the faults of rotating machinery using improved case-based reasoning is proposed in this article, which can fully utilize the experience and intelligence of previous cases to realize an intelligent diagnosis. Especially, the identification process of a new fault state is designed to address the problem that the state not included in the case base cannot be automatically recognized. The presented approach is performed to test the gear and bearing datasets. And good diagnosis results are achieved, which verifies the effectiveness of the presented approach.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rotating Machinery Based on Improved CBR

Xie

Yang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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A Bearing Signal Adaptive Denoising Technique Based on Manifold Learning and Genetic Algorithm

Yin,

Zhuang,

Sui

et al. 2024

IEEE Sensors J.

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Development of a Hierarchical Clustering Method for Anomaly Identification and Labelling of Marine Machinery Data

Velasco-Gallego,

Lazakis,

Cubo-Mateo

2024

JMSE

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The application of artificial intelligence models for the fault diagnosis of marine machinery increased expeditiously within the shipping industry. This relates to the effectiveness of artificial intelligence in capturing fault patterns in marine systems that are becoming more complex and where the application of traditional methods is becoming unfeasible. However, despite these advances, the lack of fault labelling data is still a major concern due to confidentiality issues, and lack of appropriate data, for instance. In this study, a method based on histogram similarity and hierarchical clustering is proposed as an attempt to label the distinct anomalies and faults that occur in the dataset so that supervised learning can then be implemented. To validate the proposed methodology, a case study on a main engine of a tanker vessel is considered. The results indicate that the method can be a preliminary option to classify and label distinct types of faults and anomalies that may appear in the dataset, as the model achieved an accuracy of approximately 95% for the case study presented.

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Unified discriminant manifold learning for rotating machinery fault diagnosis

Cited by 3 publications

References 41 publications

Fault Diagnosis Method for Rotating Machinery Based on Improved CBR

Fault Diagnosis Method for Rotating Machinery Based on Improved CBR

A Bearing Signal Adaptive Denoising Technique Based on Manifold Learning and Genetic Algorithm

Development of a Hierarchical Clustering Method for Anomaly Identification and Labelling of Marine Machinery Data

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