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 predictive maintenance. MsFormer incorporates a Multi-scale Sampling (MS) module and a tailored position encoding mechanism to capture sequential correlations across multi-streaming service data. Additionally, to accommodate data-scarce service environments, MsFormer adopts a lightweight attention mechanism with straightforward pooling operations instead of self-attention. Extensive experiments on real-world datasets demonstrate that the proposed framework achieves significant performance improvements over state-of-the-art methods. Furthermore, MsFormer outperforms across industrial devices and operating conditions, demonstrating strong generalizability while maintaining a highly reliable Quality of Service (QoS).

翻译：提供可靠的预测性维护是保障制造设备高可用性的关键工业AI服务。现有深度学习方法在此类任务中呈现出有竞争力的结果，但缺乏通用的服务导向框架来捕捉工业物联网传感器数据中的复杂依赖关系。尽管基于Transformer的模型展现出强大的序列建模能力，但将其直接部署为稳健的AI服务面临显著瓶颈。具体而言，实际服务环境中采集的流式传感器数据因机器工作原理驱动，常呈现多尺度时间相关性。此外，用于训练故障时间预测服务的数据集规模通常有限。这些问题对直接应用现有模型作为鲁棒预测性服务构成了重大挑战。为解决上述难题，我们提出MsFormer——一种轻量级多尺度Transformer，旨在作为统一AI服务模型用于可靠的工业预测性维护。MsFormer包含多尺度采样模块与定制化位置编码机制，以捕获多流服务数据中的序列相关性。同时，为适应数据稀缺的服务环境，MsFormer采用轻量级注意力机制与简易池化操作替代自注意力。基于真实数据集的广泛实验表明，所提框架较现有最优方法实现了显著性能提升。此外，MsFormer在不同工业设备与运行条件下均表现优异，在保持高度可靠服务质量的同时展现出强泛化能力。