摘要
Reliability assessment plays a pivotal role in engineering: it prevents equipment failures, enables proactive maintenance planning, and sustains long-term, efficient operation. By assessing reliability, we gain deeper insight into performance characteristics, detect potential anomalies early, and act in advance-reducing downtime risk, improving productivity, and supporting sustainable operations. Traditional workflows relied on manual feature selection and extraction, which are labor-intensive. Recent advances in deep learning-such as Convolutional Neural Networks (CNNs)have provided powerful tools for feature extraction, yet they do not fully capture temporal dynamics or handle measurement noise. Moreover, many prior Health Index (HI) labeling schemes used linear, time-based definitions that do not faithfully reflect real degradation trajectories. This study proposes a new framework for HI prediction and lifetime estimation that integrates WaveletKernelNet (WKN), Bidirectional Gated Recurrent Units (BiGRU), Multi-Head Self-Attention (MSA), and a Simplified Swarm Optimization (SSO) algorithm. WKN-BiGRU-MSA extracts deep features from vibration signals and constructs the HI, while SSO optimizes the neural architecture to enhance generalization. We further introduce a novel HI labeling method-a two-stage label inspired by the bathtub curve-to better align model outputs with real-world degradation. Experiments on public rolling-bearing datasets validate the effectiveness of the proposed approach. Overall, our method extracts informative features from vibration data and yields reliable estimates. © 2025 IEEE.