Lightweight Convolutional Transformers Enhanced Meta-Learning for Compound Fault Diagnosis of Industrial Robot

Abstract

Recent advance of deep learning has seen remarkable progress in compound fault diagnosis modelling for industrial robots. Nevertheless, the data scarcity of compound fault samples jeopardizes the modelling performance of deep learning algorithms. Meta learning has become an effective tool in few-shot fault diagnosis modelling. However, due to the training instability of meta learning, it is challenging to deploy advanced networks such as Transformers as the base learner due to the extremely large model size. Therefore, this study proposes a lightweight convolutional Transformers (LCT) network enhanced meta learning (Meta-LCT) method to achieve accurate compound fault diagnosis with limited compound fault samples. Specifically, the LCT is firstly designed by taking advantage of linear spatial reduction (LSR) attention and spatial pooling mechanism to achieve high computational efficiency. LCT is adopted as the base learner in the Meta-SGD algorithm, and then the meta-training is performed based on the single fault data. Subsequently, the limited compound fault samples are used in the meta testing stage to obtain a compound fault diagnosis model. An experimental study based on the real-world compound fault dataset of industrial robots is presented. The experimental results indicate that the proposed Meta-LCT can achieve the compound fault diagnosis accuracy of 81.1% when only 40 data samples in each compound fault category are available.