Predictive Inspection Scheduling for Failure-Time Maintenance Windows
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Solution Overview
Problem
Existing inspection systems fail to provide advanced notification of impending system failures and their tentative times, lacking the capability to schedule maintenance or repairs proactively.
Innovation Solution
An inspection system that determines an expected failure time based on sensor data and schedules maintenance or repairs within a defined window before the failure occurs, using a control system to analyze sensor data from various components and environments, and applies machine learning for predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing inspection systems monitor sensor data to detect failures, then system reliability is improved, but the ability to provide advanced notification of impending failures is lost
Solution Approach 1:
The system performs preliminary analysis of sensor data to predict future failures before they occur. By analyzing trends in sensor readings and comparing them against historical failure patterns, the system generates advance notifications of impending failures, allowing maintenance to be scheduled proactively rather than reactively.
Solution Approach 2:
The system implements a feedback loop where sensor data is continuously monitored, analyzed, and used to update failure predictions. The predictions are then communicated back to operators, creating a closed-loop system that provides ongoing advance notification and enables continuous improvement of maintenance scheduling based on actual system behavior.
2Ease of operation
If maintenance is scheduled based on fixed intervals, then ease of operation is improved, but system productivity deteriorates due to unnecessary maintenance or insufficient preparation time
Solution Approach 1:
The system transitions from static fixed-interval maintenance scheduling to dynamic condition-based scheduling. Maintenance intervals are automatically adjusted based on real-time sensor data analysis and failure predictions, allowing the system to extend maintenance intervals when components are healthy and shorten them when degradation is detected, thereby optimizing both operational ease and productivity.
Solution Approach 2:
The system changes the parameter of maintenance timing from fixed calendar intervals to variable intervals based on actual component condition. By monitoring sensor parameters such as vibration, temperature, and performance metrics, the system determines optimal maintenance moments that maximize productivity while ensuring reliability, avoiding both premature and delayed maintenance.
Data Source
AI summary
An inspection system and method of operation may include receiving sensor data for a component of a powered system, and determining an expected failure time of the component at which the component is expected to fail based at least in part on the sensor data. The expected failure time occurring on a timeline. A window start time may be determined on the timeline that is after a current time but is prior to the expected failure time. The window start time and the expected failure time may define a window time range that extends between the window start time and the expected failure time on the timeline. One or both of a repair action or a maintenance action of the powered system may be scheduled at a scheduled time occurring during the window time range and at a time prior to the expected failure time on the timeline.