A Novel Cross-Sensor Self-Supervised Learning Method for Rotating Machinery Fault Diagnosis

Abstract

Fault diagnosis is crucial in mechanical prognostics and health management. However, fault features extracted from single-sensor data are limited in complex operating environments. Extracting complementary and robust fault features from multi-sensor monitoring data is essential, especially under limited labeled samples. Leveraging the advantages of self-supervised learning, we propose a novel cross-sensor self-supervised learning (CSSL) method for rotating machinery fault diagnosis under limited sample conditions. Our method employs contrastive learning across multiple sensors, including both intra-sensor and inter-sensor contrastive learning, to derive robust cross-sensor fault representations. The efficacy of our approach is substantiated on two benchmark datasets, revealing superior classification performance. Furthermore, the experimental results under various operating conditions demonstrate outstanding performance and solid robustness.