2022
DOI: 10.1155/2022/4340817
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Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Abstract: Aiming at the problems of the traditional industrial robot fault diagnosis model, such as low accuracy, low efficiency, poor stability, and real-time performance in multi-fault state diagnosis, a fault diagnosis method based on DBN joint information fusion technology is proposed. By studying the information processing method and the deep learning theory, this paper takes the fault of the joint bearing of the industrial robot as the research object. It adopts the technique of combining the deep belief network (… Show more

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Cited by 13 publications
(4 citation statements)
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“…In [14], a knowledge driven DBN for diagnosing gearbox malfunctions was developed with the confidence and categorization principles. In [15], a DBN conjoint data integration system was explored for manufacturing robot fault diagnostics.…”
Section: Introductionmentioning
confidence: 99%
“…In [14], a knowledge driven DBN for diagnosing gearbox malfunctions was developed with the confidence and categorization principles. In [15], a DBN conjoint data integration system was explored for manufacturing robot fault diagnostics.…”
Section: Introductionmentioning
confidence: 99%
“…Then, a normalized feature vector is established for reconstructing energy entropy, and the normalized feature vector is used as an input for DBN. Finally, a combination of DBN and wavelet energy entropy technology is used for the fault diagnosis of industrial robots [9]. However, most of these studies require other algorithms to assist in feature extraction, or to use other algorithms to convert the original signal into an image before conducting fault diagnosis.…”
Section: Introductionmentioning
confidence: 99%
“…It is used in defect diagnosis, anomaly detection, reliability, inference, prognosis, and early prediction due to its usual features [19][20][21][22][23]. Multi-source sensor information fusion for mechanical fault diagnosis is still in its infancy due to its complexity and feature extraction and fusion issues [24,25]. Due to the rapid development of deep learning-related research results, designing a complete fault diagnosis system based on multi-source information fusion using deep learning to realize the algorithm structure, including data preprocessing, classifiers, and evidence fusion, has become important [26,27].…”
Section: Introductionmentioning
confidence: 99%