A Meta-Learning Method for Electric Machine Bearing Fault Diagnosis Under Varying Working Conditions With Limited Data

Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This article proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric machines under varying working conditions with limited data. The fault diagnosis under various working conditions is cast as a few-shot classification problem, which is solved using a model-agnostic meta-learning-based model. Specifically, a meta-learner is first trained using a series of interrelated fault-diagnosis tasks under various working conditions. During this stage, the gradient-by-gradient rule is utilized for parameter optimization to achieve an effective representation of these tasks. Then, the parameters of the meta-learner are refined on a new task. This technique can achieve fast adaptation to new tasks by utilizing only few-shot samples. The proposed method can obtain high fault-detection accuracy under various working conditions when only a limited amount of data is available. Comparative tests among various methods were carried out on the Case Western Reserve University Bearing Dataset and the Paderborn University Rolling Bearing Dataset. The results show that the proposed model performs better than other state-of-the-art methods under various working conditions; our method has stronger generalization ability and faster adaptation ability. The fault diagnosis accuracy for both datasets was at least 99%, which proves that the proposed strategy can be flexibly applied to various scenarios.

Automated summary

This article proposes a novel meta-learning-enabled method for effective detection of fault in rolling bearings of electric machines with limited data. The method utilizes a model-agnostic meta-learning-based model to solve the few-shot classification problem of fault diagnosis under various working conditions. The results show that the proposed method outperforms other state-of-the-art methods, achieving high fault-detection accuracy and demonstrating stronger generalization and faster adaptation abilities.