Equipment Supervision Model for Predictive Maintenance Simulation
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Solution Overview
Problem
Existing methodologies for equipment reliability, such as predictive maintenance, physical models, and bench tests, lack simulation capabilities to fully understand the impact of manufacturing, maintenance, and equipment use on reliability, leading to inaccurate and insufficiently proactive failure predictions and maintenance recommendations.
Innovation Solution
A digital system that generates a behavioral model specific to each piece of equipment, correlating its manufacturing, maintenance, and usage history to simulate its behavior over the long term, providing optimal maintenance decisions and usage limits to ensure zero downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive maintenance, physical models, and bench tests are used for equipment reliability, then maintenance can be performed based on historical data, but simulation capabilities to fully understand the impact of manufacturing, maintenance, and equipment use on reliability are lacking
Solution Approach 1:
The patent creates a digital twin (virtual model) that copies the physical equipment's behavior, manufacturing history, maintenance history, and usage conditions. This virtual model enables simulation of equipment reliability under various scenarios without needing physical experiments, thus resolving the contradiction between maintaining reliability and losing simulation capability.
Solution Approach 2:
The system performs preliminary simulations using the virtual model to predict equipment behavior and identify optimal maintenance strategies before actual maintenance activities are carried out. This allows stakeholders to evaluate different maintenance scenarios in advance, improving reliability while maintaining simulation capabilities.
2Productivity
If existing methodologies are used, then maintenance decisions can be made based on general patterns, but failure predictions are inaccurate and insufficiently proactive
Solution Approach 1:
The virtual model captures the specific local characteristics of each individual piece of equipment, including its unique manufacturing variations, maintenance history, and usage conditions. This individualized approach replaces generic maintenance patterns with equipment-specific predictions, improving both accuracy and proactivity while maintaining efficiency.
Solution Approach 2:
The system continuously updates the virtual model with actual equipment data from sensors and maintenance records, creating a feedback loop that refines failure predictions over time. This ongoing calibration improves prediction accuracy while maintaining efficient automated decision-making.
3Reliability
If individualized maintenance programs are determined for each equipment, then optimal maintenance can be achieved, but data processing and model generation complexity increases
Solution Approach 1:
The virtual model platform serves multiple functions: it stores equipment data, simulates behavior, predicts failures, optimizes maintenance schedules, and evaluates different scenarios. This multi-functional approach enables individualized maintenance programs for each equipment while using a standardized platform, thus managing complexity while achieving optimization.
Data Source
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AI summary
The present invention relates to a system (1) for supervision of the operation and maintenance of an item of equipment (2) in a facility (3), in which the equipment (2) is operated up to maintenance (6) to be performed, with at least one subsequent projected maintenance (7), inducing a manufacturing and maintenance log (9), a usage log (11), a log (120) of statuses (13). A correlation (14) is determined between causes and consequences of aging of the equipment (2), by characterizing tasks (90) and conditions (110) impacting the status (13). For other equipment, data corresponding to said correlation (14) is recovered and extracted, in order to train a virtual model (16). The day before the maintenance (6) to be carried out, based on the the logs (9,11), tasks (90) of the maintenance (6) to be carried out and projected conditions (110) of a scenario (8), said model (16) generates a projected status (130) compared to a minimal operating status (17) for said equipment (2).