Industrial robot is one kind of complex infrastructure in industrial production and applications. Fault diagnosis is an important part of the intelligent application and monitoring of industrial robots. For multi-axis industrial robot compound fault prediction and diagnosis problem, this paper proposes a fault diagnosis model based on improved multi-label one-dimensional convolutional neural network (ML-SRIPCNN-1D). Firstly, the compound fault data set is enhanced by random sampling and Mixup. Then, the single fault data and compound fault data were trained end-to-end by the improved multi-label one-dimensional convolutional neural network. Finally, accurate diagnosis and prediction of compound faults of industrial robots are implemented. The compound fault data set was derived from a company's multi-axis industrial robot. The characteristic variables of fault diagnosis are torque, current, velocity, position, etc. Compared with SRIPCNN-1D, MLCNN, WT-MLCNN, T-FSM-MLCNN, ELM + AE + SVM, LMD + TDSF + ML-KNN models, the average diagnosis accuracy of ML-SRIPCNN-1D reached 98.67%. The model has good diagnosis effect and high accuracy for the prediction and diagnosis of industrial robot compound fault.
CITATION STYLE
Li, P., Xiao, H., Jiang, W., & Ning, D. (2021). Compound Fault Diagnosis of Industrial Robot Based on Improved Multi-label One-Dimensional Convolutional Neural Network. In Communications in Computer and Information Science (Vol. 1454 CCIS, pp. 205–216). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7502-7_23
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