Predictive Maintenance Using Latent Features Without Failure Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for predictive analysis and process control rely heavily on failure data from sensors, which limits their effectiveness in scenarios with limited failure data, necessitating new methods that can function without extensive failure data.
Innovation Solution
A method and system that receive entity data, determine latent features through physical simulations, and use response reconstructions to predict machine health and optimize maintenance, even in the absence of large failure datasets, by combining sensor data with physical models to identify safe and unsafe operating regions and track machine performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional predictive analysis systems use sensor failure data to predict machine failures, then prediction accuracy improves, but the system becomes ineffective when failure data is limited
Solution Approach 1:
The patent introduces latent features as intermediary variables that bridge sensor data and failure predictions. These latent features capture underlying machine states and degradation patterns, enabling accurate predictions even when direct failure data is scarce. The latent features act as mediators that translate limited sensor observations into reliable failure predictions.
Solution Approach 2:
The patent transforms the prediction problem from direct sensor-to-failure mapping into a multi-dimensional space involving latent features. By projecting sensor data into latent feature space and then to failure predictions, the system creates additional dimensional pathways for information flow, enabling robust predictions with limited data.
2Reliability
If maintenance is performed frequently to prevent machine failures, then machine reliability improves, but productivity decreases due to unnecessary maintenance interruptions
Solution Approach 1:
The system performs preliminary identification of machines at risk of failure by analyzing latent features and trends before actual failures occur. This enables targeted maintenance scheduling that addresses only those machines needing intervention, avoiding unnecessary maintenance on healthy machines and preserving productivity.
Solution Approach 2:
The maintenance strategy transitions from static scheduled maintenance to dynamic condition-based maintenance. The system continuously monitors latent features and adjusts maintenance recommendations in real-time based on actual machine conditions, enabling flexible scheduling that optimizes both reliability and productivity.
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.