MQKIN: Manufacturing Quality Knowledge-Driven Interpretable Fault Diagnosis Network for Robotic Grinding Equipment

Abstract

Despite of the fast development of deep learning networks, its inexplicability poses a low credibility challenge for fault diagnosis methods based on them. This article proposes an interpretable fault diagnosis network for robotic grinding equipment driven by the knowledge of grinding process. The network consists of a vibration imaging module, a grinding quality quantification module, an auxiliary learning module, and a fault diagnosis module. In addition, the developed synchronous algorithm is embedded in the network to update the weights and coefficient matrix combinations, which can reconstruct the vibration images from the wavelet domain to be recognized by convolution kernels more easily. Besides, through auxiliary learning, the grinding knowledge can be learned by the network to endow the results with interpretability. Finally, the comparative experiment shows that the proposed method has a maximum accuracy of 4.25%, 7.25%, 2.25%, 3.25%, and 1.25% higher than the five popular state-of-the-art fault diagnosis algorithms, respectively; the ablation experiment shows that the proposed algorithm has a maximum accuracy of 6.25%, 12.5%, and 16.25% higher than each sub-algorithm, respectively; by analyzing the learned weights of the network, it can be concluded that the proposed network has successfully learned the potential features of grinding knowledge with satisfactory performance.Note to Practitioners—In the autonomous robotic grinding system, the reliability of robotic grinding equipment is a prerequisite for ensuring high-quality grinding of thin-walled parts for large equipment such as aircraft, high-speed rail, and ships. In robotic grinding manufacturing, the information of grinding quality is the most direct feedback of valuable information from the manufacturing system. Based on the causal relationship between the fault status of grinding equipment and the grinding quality of workpiece, this article develops an interpretable fault diagnosis network incorporated with the grinding knowledge, which treats grinding quality information as physical knowledge and provides credibility to the fault diagnosis results. Therefore, this article improves the interpretability of the fault diagnosis model in practical applications through the credibility of data and grinding knowledge, which improves the applicable potential of fault diagnosis methods based on deep learning in industry.