Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

TL;DR

This paper introduces a modified CGAN framework that synthesizes fault data for bearings under various operational conditions, aiding fault diagnosis when real fault data is scarce or unavailable.

Contribution

A novel CGAN-based algorithm that generates fault data from normal data across different conditions, enhancing data availability for fault diagnosis.

Findings

Generated fault data closely resembles real fault data.

Improved classifier performance with synthesized data.

Validated on real-world bearing dataset.

Abstract

Bearings are one of the vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data is ample as systems usually work in desired conditions. On the other hand, fault data is rare, and in many conditions, there is no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. Trained on the normal and fault data on any actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. Several state-of-the-art classifiers and visualization models are implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.