Intelligent Fault Diagnosis of Rotary Machines: Conditional Auxiliary Classifier GAN Coupled With Meta Learning Using Limited Data

Abstract

The industrial advancement has promoted the development of deep learning (DL)-based intelligent fault diagnosis methods for condition-based maintenance (CBM). Though these methods rely on large dataset for training, the collection of large number of fault samples is not practically feasible. For this purpose, generative adversarial networks (GANs) are capable to generate high-quality synthetic samples. However, the problem still persists with the training of GAN using limited fault samples that are present in practical conditions. This article proposes a novel conditional auxiliary classifier GAN framework coupled with model agnostic meta learning (MAML) to resolve this problem. The objective is to initialize and update the network parameters using MAML instead of regular stochastic gradient learning. This modification enables GAN to learn the task of synthetic sample generation using the limited training dataset. The effectiveness of the proposed framework has been compared with several famous state-of-the-art intelligent fault diagnosis methods existing in the literature. The comparative performance has been validated on benchmarked datasets, i.e., air compressor and bearing datasets collected from a single-stage reciprocating air compressor. The proposed framework is able to achieve the classification accuracy of 99.26% and 98.55% for bearing and air compressor datasets, respectively, with only ten samples per class. Moreover, a real-time case study is performed to validate the proposed method in real time.