Production Asset Time-Series Foundation Model for Cross-Asset Forecasting
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Solution Overview
Problem
Conventional time-series models face scalability issues and struggle to effectively generalize to specific asset data in production facilities, particularly for equipment like compressors, pumps, and heat exchangers, due to their individual training approach and the heterogeneity of time-series sensor readings.
Innovation Solution
A production asset time-series foundation model is developed using synthetic data and domain knowledge to handle multivariate and multimodal data, incorporating physics equations and metadata, allowing for zero-shot and few-shot predictions and adapting to various equipment types with minimal additional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional time-series models use individual training for each task in a single domain, then the model can effectively capture temporal dependencies for that specific task, but the model suffers from scalability issues and cannot generalize to other assets or tasks
Solution Approach 1:
The patent applies universality by training a single foundation model on multivariate time-series data from multiple assets and tasks simultaneously. The model learns domain-general temporal patterns that can be transferred across different production assets (compressors, pumps, heat exchangers) and tasks (forecasting, anomaly detection, imputation), eliminating the need for individual training per task while maintaining effectiveness through shared representation learning.
2Adaptability or versatility
If conventional models are trained on heterogeneous time-series sensor readings from different assets, then the model may capture diverse patterns, but the heterogeneity of data makes it difficult to derive unified knowledge for decision-making
Solution Approach 1:
The patent merges heterogeneous time-series data from multiple assets into a unified training framework. By combining data from compressors, pumps, heat exchangers, and other production assets with their respective sensor readings, the foundation model learns unified temporal patterns and relationships that generalize across asset types, enabling effective knowledge extraction and decision-making despite data heterogeneity.
3Adaptability or versatility
If a foundation model is trained on large amounts of multivariate time-series data from multiple assets, then the model achieves better generalization and scalability, but the training complexity and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-training a foundation model on large-scale multivariate time-series data from multiple production assets before deploying it to specific tasks. This pre-training phase establishes a robust temporal understanding and feature representation that can be fine-tuned or directly applied to various downstream tasks, reducing the need for extensive task-specific training and simplifying subsequent deployment.
Data Source
AI summary
A method for building a production asset time-series foundation model includes receiving input data related to equipment. The input data includes (1) domain knowledge and equations related to the equipment and (2) metadata related to the equipment. The method also includes training the production asset time-series foundation model based upon the input data to produce a trained production asset time-series foundation model. The method also includes performing a downstream task using the trained production asset time-series foundation model. The downstream task includes forecasting, imputation, anomaly detection, history matching, health monitoring, or a combination thereof.