An attention-enhanced multi-modal deep learning algorithm for robotic compound fault diagnosis

Compound fault diagnosis plays a critical role in lowering the maintenance time and cost of industrial robots. With the advance of deep learning and industrial big data, a compound fault diagnosis model can be established through a data-driven approach. However, current methods mainly focus on the single fault diagnosis of assets, which cannot achieve satisfactory performance for compound fault diagnosis. This study proposes a compound fault diagnosis algorithm for an industrial robot based on multi-modal feature extraction and fusion. Firstly, the multi-head self-attention enhanced convolution neural network module and long short-term memory network module are adopted to learn the fault-related features from different perspectives simultaneously. The local and global features extracted by the aforementioned modules are then fused for subsequent compound fault classification. An experimental study was implemented based on real-world robotic sensor data. The experimental results indicated that the proposed multi-modal algorithm shows merits in compound fault diagnosis in comparison with other state-of-the-art methods.

Automated summary

In this study, a compound fault diagnosis algorithm for an industrial robot based on multi-modal feature extraction and fusion is proposed. By adopting the multi-head self-attention enhanced convolution neural network module and long short-term memory network module, fault-related features are learned from different perspectives simultaneously, and the local and global features are fused for accurate compound fault diagnosis.