A frugal predictive maintenance method, producing a corresponding computer program and computer-readable media.
The frugal predictive maintenance process addresses the challenges of data scarcity and irregular usage by creating a digital twin and using AI to process minimal data, achieving effective real-time health monitoring and maintenance inference for machine parks.
FR3142812B1Active Publication Date: 2025-03-28KNDS FRANCE
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Patent Information
- Application Number
- FR2022012542
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2022-12-02
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2042-12-02
AI Technical Summary
Technical Problem
Current predictive maintenance techniques for machine parks, especially in the military sector, face challenges due to the scarcity of data and irregular usage patterns, making it difficult to achieve reliable predictions.
Method used
A frugal predictive maintenance process that combines data-centered and model-driven approaches by creating a digital twin of machines, using AI to process minimal data, and continuously learning from sensor measurements to detect maintenance needs.
Benefits of technology
This process enables real-time health monitoring of machines, reduces data requirements, and improves maintenance inference, making it suitable for both military and civilian vehicle parks with varying usage profiles.
✦ Generated by Eureka AI based on patent content.
Abstract
Frugal Predictive Maintenance Method, Computer Program and Corresponding Computer-Readable Media. The invention relates to a predictive maintenance method for a fleet of machines, consisting of building a digital twin of the fleet, selecting a part to monitor, listing relevant parameters to measure on the monitored part, training the digital twin by simulating failures on a machine in the fleet with a known degree of wear and equipped with sensors measuring the relevant parameters, and deploying the digital twin for the fleet of machines equipped with sensors measuring the relevant parameters to perform predictive maintenance. Figure to be published with the abstract: Figure 1
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