Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots

•A simple yet effective attitude data acquisition strategy is designed for industrial robots.•A novel feature extractor, integrating multi-scale convolutional neural network and capsule network is proposed.•Fault diagnosis models under different application scenarios are built based on the feature extractor.•The superiority of the feature extractor is demonstrated on a series robot and a parallel robot. Effective fault diagnosis is important to ensure the reliability, safety, and efficiency of industrial robots. This article proposes a simple yet effective data acquisition strategy based on transmission mechanism analysis, using only one attitude sensor mounted on an end effector or an output component to monitor the attitude of all transmission components. Unlike widely used vibration-monitoring signals, attitude signals can provide fault features reflecting spatial relationships. Using one attitude sensor facilitates the data collection, but weakens fault features and introduces strong background noise in attitude signals. To learn discriminative features from the attitude data collected by the attitude sensor, a multiscale convolutional capsule network (MCCN) is proposed. In MCCN, integrating low-level and high-level features in a convolutional neural network (CNN) as multiscale features is conductive to noise reduction and robust feature extraction, and a capsule network (CapsNet) is used to recognize the spatial relationships in attitude data. The extracted multiscale features in CNN and the spatial-relational features in CapsNet are fused for effective fault diagnosis of industrial robots. The performance of MCCN is evaluated by attaching a softmax-based classifier and integrating it into different transfer learning frameworks to diagnose faults in industrial robots under single and variable working conditions, respectively. Fault diagnosis experiments were conducted on a 6-axis series industrial robot and a parallel robot-driven 3D printer. The superiority of the proposed MCCN was demonstrated by comparing its performance with the other feature learning methods.