Failure Probability Scheduling for Planned Equipment Maintenance
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Solution Overview
Problem
Current predictive maintenance solutions based on IoT sensors provide only short-term warnings, often too late for scheduled maintenance, leading to inadequate scheduling and potential equipment failures, and are limited by the subjective judgment of human inspectors and labor shortages.
Innovation Solution
A system that uses feedback loops and probability-based models to predict equipment failures over long terms by analyzing IoT sensor data and causal factors, allowing for advanced scheduling of maintenance within existing maintenance schedules, and automating the process to reduce reliance on human judgment and labor shortages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IOT sensors are used to detect abnormal operating conditions, then failure detection capability is improved, but maintenance scheduling timeliness deteriorates because detection occurs too late for planned maintenance shutdowns
Solution Approach 1:
The system performs preliminary maintenance actions based on probabilistic predictions before actual failure occurs. By calculating the probability of failure over time and comparing it against a threshold, the system schedules maintenance during planned shutdowns before the component actually fails, rather than reacting to abnormal conditions after they manifest.
Solution Approach 2:
The maintenance scheduling system dynamically adjusts based on changing failure probabilities. Instead of fixed schedules or reactive responses to abnormal conditions, the system continuously updates failure probability assessments and adapts maintenance timing to match both equipment risk profiles and operational constraints.
2Ease of operation
If fixed maintenance schedules are used, then maintenance planning is simplified, but maintenance effectiveness deteriorates due to conservative timing and potential over-maintenance
Solution Approach 1:
The system changes the key parameter from fixed time intervals to dynamic failure probability thresholds. Maintenance is triggered when the calculated probability of failure exceeds a predetermined threshold, allowing schedules to be both simple to operate and precisely targeted to actual equipment needs.
3Adaptability or versatility
If human inspection and judgment are used for maintenance decisions, then flexibility in assessing component state is improved, but objectivity and consistency deteriorate due to subjective judgment
Solution Approach 1:
The system replaces human mechanical inspection and judgment with automated computational analysis. A processor calculates failure probabilities based on sensor data and historical information, providing objective, consistent, and reproducible maintenance decisions without human subjectivity while maintaining adaptability through configurable probability thresholds.
4Reliability
If short-term failure prediction based on IOT sensors is used, then immediate failure risk identification is improved, but maintenance scheduling adequacy deteriorates because prediction horizon is too short for planned shutdowns
Solution Approach 1:
The system performs preliminary maintenance based on probabilistic predictions made well before failure becomes certain. By continuously assessing failure probability from installation or last maintenance through current operation, the system identifies the optimal window for scheduled maintenance that provides adequate lead time for planning while ensuring maintenance occurs before actual failure.
Data Source
AI summary
Systems, methods, and other embodiments associated with long-term predictions for specific maintenance within a planned maintenance schedule. In one embodiment, a method includes updating a failure probability curve for a component part of a device based at least in part on data obtained from a sensor associated with the component part; determining based at least in part on the updated failure probability curve that a likelihood of failure for the component part following a first upcoming planned maintenance and before a second upcoming planned maintenance exceeds a threshold; and transmitting a work order for specific maintenance to reduce the likelihood of failure of the component part to be performed during the first upcoming planned maintenance.