Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models
arXiv:2606.05481v1 Announce Type: new Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets. These tasks are central to maintenance planning, but industrial PHM data are often fragmented, partially observed, and poorly labeled, which hinders supervised learning. Foundation models offer a route toward reusable predictive systems, yet most time-series foundation models are designed for forecasting and assume long, coherent, regularly sampled sequences. To address
The development of general-purpose foundation models is increasingly applied to specialized domains, and the problem of fragmented and poorly labeled industrial data for predictive maintenance is a significant challenge in current AI applications.
This development addresses a critical limitation in current industrial AI, enabling more robust and data-efficient prognostics and health management (PHM) that can significantly improve operational efficiency and reduce maintenance costs across diverse industries.
The ability to develop unified, data-efficient PHM systems will move away from bespoke, data-intensive models towards more generalized and adaptable AI solutions for maintaining engineered assets.
- · Industrial IoT platforms
- · Smart manufacturing companies
- · AI model developers
- · Heavy industry operators
- · Traditional PHM consultancies
- · Companies reliant on large, clean datasets
- · Legacy predictive maintenance software
More reliable industrial operations and reduced unplanned downtime due to improved predictive maintenance capabilities.
Accelerated adoption of AI in industrial settings, leading to higher automation and optimized asset management across sectors.
New standards and interoperability requirements for industrial data to facilitate the training and deployment of advanced foundation models.
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