Incorporate Rotational Speeds Into Deep Neural Network for Machinery Health Monitoring

Many machines generally operate under time-varying speed conditions. The significant and frequent changes of speeds often lead to missed defect detections and false alarms, which hinders the further applications of health monitoring technology. In this article, a novel self-supervised learning motivated network that is composed of extractor, projector, spyder and detector, i.e., EPSDNet, is proposed for self-adaptive health monitoring of machines under time-varying speed conditions. First, contrastive learning is used in EPSDNet to implement self-extraction of trend manifold over speeds, which adjusts the representational features and reduces their entropy. Second, a nonlinear regression network, i.e., Spyder, is proposed to identify the relationship between vibration signals and corresponding rotational speeds, which facilitates the adaptive feature alignment for each transient speed condition. Finally, a refined skip connection-based feature fusion is proposed to balance the calculation cost and signal reconstruction among encoder and decoder of detector. The real-time health indicator can be obtained based on the reconstruction error of detector for machinery. The effectiveness of EPSDNet is validated on two bearing test rigs with time-varying rotational speeds. The results show that EPSDNet outperforms other state-of-the-art methods for machinery health monitoring, which provides a novel approach to address the challenging issue of time-varying speed conditions.