Industrial robots are prone to failure due to harsh working environments, which affects movement accuracy. The fault diagnosis of industrial robots has become an indispensable part of robot collaborative maintenance in intelligent manufacturing. Most existing diagnostic methods only use a single data source, and the diagnostic accuracy will be affected due to signal acquisition errors and noise interference. This paper proposes a multi-source data fusion and channel attention convolutional neural network (MD-CA-CNN) for fault diagnosis of multi-joint industrial robots. The network takes the time domain data and time-frequency domain data of the vibration signal, torque signal, and current signal of the six joints of the robot as input. Then, we realize the diagnosis of the faults by using a Softmax Classifier layer after the two parts of feature extraction and feature fusion. In addition, a channel attention mechanism is developed. It acts on the two parts of feature extraction and feature fusion, respectively. It assigns weights to different source data and weights to time-domain and time-frequency domain features. Finally, we established a test bench to compare the proposed method with the deep learning algorithm that only uses multi-data source fusion, the deep learning algorithm that only uses a single data source, and the commonly used machine learning algorithm. The results show that the MD-CA-CNN model proposed in this paper has the highest accuracy and stability, reaching 95.8% ± 0.39%, which verifies the method’s effectiveness.

In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms.