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 (DBN) and wavelet energy entropy, and the fault diagnosis of industrial robot is studied. The wavelet transform is used to denoise, decompose, and reconstruct the vibration signal of the joint bearing of the industrial robot. The normalized eigenvector of the reconstructed energy entropy is established, and the normalized eigenvector is used as the input of the DBN. The improved D-S evidence theory is used to solve the problem of fusion of high conflict evidence to improve the fault model’s recognition accuracy. Finally, the feasibility of the model is verified by collecting the fault sample data and creating the category sample label. The experiment shows that the fault diagnosis method designed can complete the fault diagnosis of industrial robot well, and the accuracy of the test set is 97.96%. Compared with the traditional fault diagnosis model, the method is improved obviously, and the stability of the model is good; the utility model has the advantages of short time and high diagnosis efficiency and is suitable for the diagnosis work under the condition of coexisting multiple faults. The reliability of this method in the fault diagnosis of the joint bearing of industrial robot is verified.

An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

“…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.…”

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM

“…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.…”

Research on mechanical fault diagnosis based on MADS evidence fusion theory

“…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].…”