Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise

TL;DR

This paper presents a model-agnostic meta-learning approach for fault diagnosis in induction motors, enabling accurate detection under limited data, varying conditions, and drive noise, outperforming existing methods.

Contribution

It introduces a novel meta-learning framework for mechanical fault detection that adapts quickly to new conditions with minimal data, addressing real-world industrial challenges.

Findings

Fault detection accuracy reaches at least 99%.

The method outperforms other advanced techniques.

Rapid adaptation with few samples is achieved.

Abstract

Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy and faulty samples and the scarcity of data representing faulty conditions. This research introduces an innovative meta-learning approach to address these issues, focusing on mechanical fault detection in induction motors across diverse operating conditions while mitigating the adverse effects of drive noise in scenarios with limited data. The process of identifying faults under varying operating conditions is framed as a few-shot classification challenge and approached through a model-agnostic meta-learning strategy. Specifically, this approach begins with training a meta-learner across multiple interconnected fault-diagnosis tasks conducted under different operating conditions. In this stage, cross-entropy is utilized to optimize parameters and develop a robust representation of the tasks. Subsequently, the parameters of the meta-learner are fine-tuned for new tasks, enabling rapid adaptation using only a small number of samples. This method achieves excellent accuracy in fault detection across various conditions, even when data availability is restricted. The findings indicate that the proposed model outperforms other sophisticated techniques, providing enhanced generalization and quicker adaptation. The accuracy of fault diagnosis reaches a minimum of 99%, underscoring the model's effectiveness for reliable fault identification.