[2603.23076] MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices

Computer Science > Machine Learning arXiv:2603.23076 (cs) [Submitted on 24 Mar 2026] Title:MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices Authors:Jiahui Zhou, Dan Li, Ruibing Jin, Jian Lou, Yanran Zhao, Zhenghua Chen, Zigui Jiang, See-Kiong Ng View a PDF of the paper titled MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices, by Jiahui Zhou and 7 other authors View PDF HTML (experimental) Abstract:Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods present competitive results on such tasks but lack a general service-oriented framework to capture complex dependencies in industrial IoT sensor data. While Transformer-based models show strong sequence modeling capabilities, their direct deployment as robust AI services faces significant bottlenecks. Specifically, streaming sensor data collected in real-world service environments often exhibits multi-scale temporal correlations driven by machine working principles. Besides, the datasets available for training time-to-failure predictive services are typically limited in size. These issues pose significant challenges for directly applying existing models as robust predictive services. To address these challenges, we propose MsFormer, a lightweight Multi-scale Transformer designed as a unified AI service model for reliable industrial predictiv...