Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks

TL;DR

This paper introduces SCN-CEL, a cloud ensemble learning approach using stochastic configuration networks for fault diagnosis in rolling bearings, especially effective with limited samples and uncertain fault information.

Contribution

It develops a novel cloud feature extraction and sampling method combined with SCN ensemble learning to improve fault diagnosis accuracy under uncertain and few-shot conditions.

Findings

Outperforms existing methods in few-shot fault diagnosis scenarios.

Effectively characterizes fault uncertainty using cloud models.

Achieves high accuracy in distinguishing fault categories.

Abstract

Fault diagnosis of rolling bearings is of great significance for post-maintenance in rotating machinery, but it is a challenging work to diagnose faults efficiently with a few samples. Additionally, faults commonly occur with randomness and fuzziness due to the complexity of the external environment and the structure of rolling bearings, hindering effective mining of fault characteristics and eventually restricting accuracy of fault diagnosis. To overcome these problems, stochastic configuration network (SCN) based cloud ensemble learning, called SCN-CEL, is developed in this work. Concretely, a cloud feature extraction method is first developed by using a backward cloud generator of normal cloud model to mine the uncertainty of fault information. Then, a cloud sampling method, which generates enough cloud droplets using bidirectional cloud generator, is proposed to extend the cloud feature samples. Finally, an ensemble model with SCNs is developed to comprehensively characterize the uncertainty of fault information and advance the generalization performance of fault diagnosis machine. Experimental results demonstrate that the proposed method indeed performs favorably for distinguishing fault categories of rolling bearings in the few shot scenarios.