Predictive Process Control Using Physics-Simulated Failure Features
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Solution Overview
Problem
Conventional systems for predictive analysis and process control rely heavily on failure data from sensors, which is often scarce, limiting their effectiveness in predicting machine failures and maintaining optimal operational conditions.
Innovation Solution
A method and system that combine sensor data with physical simulations to identify latent features and response reconstructions, enabling predictive maintenance and process control even in the absence of large failure data sets, by determining safe and unsafe operating regions and hyper-velocity trends within these regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional predictive analysis systems use sensor failure data to predict machine failures, then prediction capability is improved, but the system becomes ineffective when failure data is scarce
Solution Approach 1:
The patent introduces physics simulations as an intermediary to generate synthetic failure data. The simulations create virtual failure scenarios that mirror real physical behaviors, enabling the system to train predictive models without relying solely on scarce real-world failure data. This intermediary layer bridges the gap between limited actual failures and the need for comprehensive training data.
Solution Approach 2:
The system creates copies of real failure scenarios through physics-based simulations. By replicating the physical mechanisms that lead to failures in a virtual environment, the system generates multiple synthetic failure examples from limited real data, effectively multiplying the available training data while preserving the underlying failure patterns.
2Reliability
If the system uses physics simulations to generate synthetic data, then predictive capability with limited failure data is improved, but computational complexity increases
Solution Approach 1:
The system performs physics simulations in advance to pre-generate synthetic failure data before actual predictive analysis is needed. This preliminary action creates a library of virtual failure scenarios that can be directly used for training models, eliminating the need to run complex simulations in real-time during actual prediction tasks.
Solution Approach 2:
The patent applies physics simulations selectively to generate only the specific synthetic data needed for training, rather than continuously running full-scale simulations. By generating excessive synthetic data in advance, the system ensures sufficient training material is available while avoiding unnecessary computational overhead during operational prediction phases.
3Quantity of substance
If the system collects more condition monitoring data, then data coverage is improved, but the proportion of useful failure examples remains low
Solution Approach 1:
Physics simulations serve as an intermediary that transforms abundant normal operation data into valuable failure examples. By applying simulated failure conditions to normal operation data, the system generates synthetic failure scenarios that preserve the richness of the original data while introducing the critical failure patterns needed for effective predictive analysis.
Data Source
AI summary
A system for predictive analysis and/or process control, preferably including one or more communication and/or computing systems, and optionally including one or more entities and/or sensors. A method for predictive analysis and/or machine operation, preferably including receiving entity data and determining one or more latent features, and optionally including determining one or more response reconstructions, determining a processed representation of the entity data, determining entity information, and/or acting based on entity information. In some embodiments, the method can additionally or alternatively include: determining segments, identifying one or more state change event occurrences, determining an event data subset based on the state change event occurrences, generating a response reconstruction using the event data subset, selecting a physical simulation, selecting one or more simulation hyperparameters for the physical simulation, running the physical simulation, and/or extracting one or more latent features from the physical simulation.