Production Facility Health Assessment Using Component-Based ML
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Solution Overview
Problem
Conventional models fail to provide an accurate assessment of the overall health of a production facility, as they focus only on individual components without considering their impact on the facility's overall health.
Innovation Solution
A method and system utilizing machine-learning (ML) models trained on physical properties and expert-defined health levels of facility components to determine the overall health, incorporating equations and weights to assess the contributions of individual components to the facility's health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional models are used to assess facility health, then individual component health can be determined, but the overall facility health cannot be accurately assessed
Solution Approach 1:
The facility is segmented into multiple components, each with its own health indicators. The ML model processes individual component data separately and then integrates them to determine overall facility health, enabling precise assessment without requiring a single complex monolithic model
Solution Approach 2:
A machine learning model acts as an intermediary between individual component health data and overall facility health assessment. The ML model synthesizes component-level information into facility-level insights, resolving the contradiction between detailed component analysis and holistic facility evaluation
2Extent of automation
If machine learning models are trained on historical data, then automated health determination is achieved, but initial data collection and model training time is required
Solution Approach 1:
Historical data is collected and the ML model is trained in advance during a preliminary phase. Once trained, the model provides automated real-time health assessments without requiring further training, converting initial time investment into ongoing automation benefits
Solution Approach 2:
The system transitions from a static manual assessment process to a dynamic automated system. The ML model adapts to new data patterns over time while providing continuous automated health determination, balancing the initial training time requirement with long-term automation gains
Data Source
AI summary
A method for determining a health of a production facility includes receiving first input data including (1) physical properties of components within the production facility at a plurality of different times and (2) the health of the production facility at the different times. The method also includes training a machine-learning (ML) model based upon the first input data to produce a trained ML model. The method also includes receiving second input data. The second input data is measured and/or received after the ML model is trained. The second input data includes the physical properties of the components within the production facility. The method also includes determining the health of the production facility using the trained ML model based upon the second input data.