Valve Lifetime Profiling for Accurate Remaining Life Prediction
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Solution Overview
Problem
Existing process control systems face challenges in accurately predicting the lifespan of valves due to varying operating conditions, such as temperature, pressure, and fluid state, which are difficult to simulate in laboratory tests, leading to incomplete and non-informative Mean Time To Failure (MTTF) and Mean Time Between Failure (MTBF) data.
Innovation Solution
A method using an integrated diagnostics module within process control devices to collect and analyze real-time operating data, combining laboratory and historical data to develop a projected remaining lifetime profile for valve components, accounting for mechanical wear and fatigue, and providing proactive maintenance alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If laboratory testing is used to determine MTTF and MTBF, then testing can be controlled and repeated, but the test conditions cannot fully simulate real operating conditions such as temperature, pressure, and fluid state variations
Solution Approach 1:
The system performs preliminary diagnostics by collecting operating condition data (temperature, pressure, fluid state) before failure occurs, storing this data in association with the valve identifier. This preliminary data collection enables accurate MTTF and MTBF predictions without requiring exhaustive laboratory testing of all possible operating conditions.
Solution Approach 2:
The system continuously monitors actual valve performance and operating conditions, comparing expected lifetime (from laboratory data) with actual usage patterns. This feedback loop allows the system to update and refine MTTF and MTBF predictions based on real-world performance, improving accuracy over time while accounting for varied operating conditions.
2Reliability
If historical service and repair data are collected from customers, then real-world operating conditions can be captured, but customers are reluctant to share data due to competitive concerns and incomplete maintenance records
Solution Approach 1:
The system enables customers to perform self-diagnosis and self-monitoring of valve conditions using the integrated diagnostics module. By collecting and analyzing operating data locally at the valve, the system eliminates the need for customers to manually report maintenance records, thereby overcoming reluctance to share data while ensuring complete and accurate information collection.
3Loss of time
If periodic diagnostics are performed on process control devices, then maintenance scheduling can be improved, but the system cannot provide precise remaining lifetime predictions due to varying operating conditions
Solution Approach 1:
The system dynamically adjusts lifetime predictions by incorporating real-time operating parameters (temperature, pressure, fluid state) into the analysis. Instead of using fixed periodic intervals, the system modifies the predicted remaining lifetime based on actual parameter variations, providing precise predictions that account for changing operating conditions between diagnostic intervals.
Data Source
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AI summary
The claimed method and system develops a useful lifetime profile for a component of a process control device, such as a valve, and uses that lifetime profile to determine a projected remaining lifetime for the device component in operation. The lifetime profile is developed from using real world operational data of similar process control devices, used under substantially the same operating conditions as to be experienced during operation. Profiles may be developed for numerous device components, from which a projected lifetime profile for the entire process control device is developed. Based on the projected remaining lifetime, notification warnings may be sent to remote computers and maintenance scheduling may be automatically achieved.