Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation

TL;DR

This paper introduces a novel constructive incremental learning approach with ensemble domain adaptation for fault diagnosis in rolling bearings, effectively handling limited data and environmental variability to improve diagnostic accuracy.

Contribution

It proposes a new CIL-EDA framework that combines cloud feature extraction, wavelet packet decomposition, and domain adaptation on stochastic configuration networks for enhanced fault diagnosis.

Findings

CIL-DA outperforms existing domain adaptation methods.

CIL-EDA achieves superior fault diagnosis accuracy in few-shot scenarios.

The approach effectively handles environmental complexity and limited data.

Abstract

Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge. Additionally, the complexity of the external environment and the structure of rolling bearings often manifests faults characterized by randomness and fuzziness, hindering the effective extraction of fault characteristics and restricting the accuracy of fault diagnosis. To overcome these problems, this paper presents a novel approach termed constructive Incremental learning-based ensemble domain adaptation (CIL-EDA) approach. Specifically, it is implemented on stochastic configuration networks (SCN) to constructively improve its adaptive performance in multi-domains. Concretely, a cloud feature extraction method is employed in conjunction with wavelet packet decomposition (WPD) to capture the uncertainty of fault information from multiple resolution aspects. Subsequently, constructive Incremental learning-based domain adaptation (CIL-DA) is firstly developed to enhance the cross-domain learning capability of each hidden node through domain matching and construct a robust fault classifier by leveraging limited labeled data from both target and source domains. Finally, fault diagnosis results are obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel ensemble learning. Experimental results demonstrate that our CIL-DA outperforms several domain adaptation methods and CIL-EDA consistently outperforms state-of-art fault diagnosis methods in few-shot scenarios.