End-of-Run Prediction for Condition-Based Equipment Maintenance
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Solution Overview
Problem
Industrial equipment and components often experience wear and degradation, leading to inefficient maintenance schedules that result in resource wastage or increased risk of unplanned outages and safety incidents, as existing methods replace components en masse regardless of their actual condition.
Innovation Solution
A computer-implemented method using operational and inspection data to train a model for predicting the end-of-run for equipment and components, allowing for personalized maintenance recommendations based on current field data, thereby optimizing replacement schedules and reducing unnecessary replacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If components are replaced based on periodic schedule, then equipment reliability is maintained, but resource wastage increases due to replacing components before wear out
Solution Approach 1:
The maintenance schedule transitions from static periodic replacement to dynamic condition-based replacement. The system continuously monitors component conditions through sensors and adjusts maintenance timing based on actual wear rates and operational conditions, replacing components only when predicted to reach end-of-run, thereby eliminating premature replacement waste while maintaining reliability
Solution Approach 2:
The system implements feedback loops where component condition data from sensors and inspections is fed into predictive models that update remaining useful life predictions. This feedback mechanism allows the maintenance schedule to adapt to actual component degradation patterns, enabling replacement at the optimal moment rather than following fixed schedules that cause resource wastage
2Loss of time
If multiple components are replaced at scheduled time, then equipment downtime is minimized, but resource wastage increases due to replacing components that do not need replacement
Solution Approach 1:
The maintenance strategy segments the equipment into individual components with independent predictive maintenance schedules. Each component is monitored and predicted to fail at different times based on its specific wear pattern. This allows components to be replaced individually at their actual end-of-run points rather than replacing all components at a single scheduled time, reducing both unnecessary resource wastage and total downtime by focusing maintenance only on components that need it
3Loss of substance
If components are run past wear out points, then resource utilization is maximized, but risk of failure increases leading to unplanned outages and safety incidents
Solution Approach 1:
The system performs preliminary actions by predicting component end-of-run points in advance using trained models and current condition data. This allows maintenance to be scheduled proactively before actual failure occurs, maximizing resource utilization by keeping components in service until their true wear-out point while avoiding unplanned outages and safety incidents through advance planning and intervention
Data Source
AI summary
A system and computer-implemented method are provided for monitoring equipment. The method includes obtaining a trained model for an item, the item comprising equipment or a component of the equipment, the model having been trained using historical operational data of the type of equipment, and historical wear data acquired by inspecting the type of equipment and/or the type of component; using the trained model to generate an end-of-run prediction for the item using current or post service field inspection data for the item; analyzing the end-of-run prediction to determine a maintenance recommendation; and generating an output based on the prediction.