Intelligent Fault Diagnosis of Rotating Machinery Driven by Physical Information and Contrastive Learning under Extreme Sample Imbalance Conditions

Abstract Rotating machinery is the common types of equipment on ship and serves as the fundamental component to ensure ship operation and functionality. Developing intelligent fault diagnosis methods for rotating machinery is critical for timely and accurate detection of potential faults, guiding effective maintenance and repair, and ultimately extending the service life of the equipment. However, the lack of sufficient fault data for rotating machinery makes it challenging to employ deep learning to build intelligent fault diagnosis model. To address this issue, this paper introduces the prior knowledge of time-series signals and the inherent differences in samples under various states of rotating machinery. By leveraging few-shot fault samples in combination with normal state data, a novel contrastive loss function is proposed. This loss function enables the effective optimization of deep learning-based model hyperparameters under the condition of extreme training sample imbalance. Additionally, a fault diagnosis algorithm is designed to achieve the diagnosis modeling and application under highly imbalanced sample conditions. The proposed method is validated through experiments on two common types of rotating machinery: gearboxes and bearings. Furthermore, the effectiveness and scalability are demonstrated on an industrial robot fault experimental platform.