Quick Learning Mechanism with Cross-Domain Adaptation for Intelligent Fault Diagnosis

TL;DR

This paper presents a rapid adaptation method for fault diagnosis models, enabling quick transfer from laboratory to industrial settings with minimal labeled data and training iterations.

Contribution

It introduces a cross-domain adaptation technique using Net2Net transformation and fine-tuning to efficiently update diagnostic models for different operating conditions.

Findings

Effective on multiple datasets with variable conditions

Requires few labeled samples and minimal training iterations

Enables quick transfer from artificial to real damage data

Abstract

The fault diagnostic model trained for a laboratory case machine fails to perform well on the industrial machines running under variable operating conditions. For every new operating condition of such machines, a new diagnostic model has to be trained which is a time-consuming and uneconomical process. Therefore, we propose a quick learning mechanism that can transform the existing diagnostic model into a new model suitable for industrial machines operating in different conditions. The proposed method uses the Net2Net transformation followed by a fine-tuning to cancel/minimize the maximum mean discrepancy between the new data and the previous one. The fine-tuning of the model requires a very less amount of labelled target samples and very few iterations of training. Therefore, the proposed method is capable of learning the new target data pattern quickly. The effectiveness of the proposed fault diagnosis method has been demonstrated on the Case Western Reserve University dataset, Intelligent Maintenance Systems bearing dataset, and Paderborn university dataset under the wide variations of the operating conditions. It has been validated that the diagnostic model trained on artificially damaged fault datasets can be used to quickly train another model for a real damage dataset.