IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring

• URL: http://arxiv.org/abs/2609.08375v1
• Date: Tue, 08 Sep 2026 07:43:04 GMT
• Title: IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
• Abstract summary: We propose the industrial process monitoring foundation model (IPM-FM)<n>It first learns general-purpose representations from unlabeled industrial process data through self-supervised pretraining.<n>It adapts to specific monitoring tasks using a small amount of task-labeled data, and finally produces calibrated predictions.
• Score: 59.59850511287819
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants. Current practice remains a one-task-one-model paradigm that is label-inefficient and prone to degradation under operating drift. Foundation models have reshaped language, vision, and generic time-series forecasting, but it has not been adapted to industrial process monitoring. This setting poses domain-specific challenges, including safety-critical decisions and asymmetric sampling between process variables and laboratory measurements. We propose the industrial process monitoring foundation model (IPM-FM). It first learns general-purpose representations from unlabeled industrial process data through self-supervised pretraining, then adapts to specific monitoring tasks using a small amount of task-labeled data, and finally produces calibrated predictions through an uncertainty-aware prediction head. IPM-FM integrates a self-supervised Informer backbone with a multi-criteria consensus feature selector, a recursive lag-feature regression head, and a calibrated Monte Carlo dropout uncertainty module. On a seven-year hydrotreater dataset for diesel flash-point soft sensing, IPM-FM attains an RMSE of 2.99, $R^2$ of 0.50, and 97\% coverage of its 95\% predictive interval, outperforming the strongest classical and from-scratch sequence baselines by 8.3\% and 14.6\% in RMSE respectively, supporting the viability of a unified pretraining--adaptation framework for industrial process monitoring.

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