MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices

TL;DR

MsFormer is a lightweight, multi-scale Transformer model designed for industrial predictive maintenance, effectively capturing complex temporal dependencies in streaming sensor data and performing well even with limited training data.

Contribution

The paper introduces MsFormer, a novel multi-scale Transformer architecture with a tailored position encoding and lightweight attention, enhancing robustness and generalizability in industrial predictive maintenance.

Findings

Significant performance improvements over state-of-the-art methods.

Effective in diverse industrial devices and operating conditions.

Maintains high reliability and QoS in real-world environments.

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 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).