Turbine Fatigue Monitoring via Data Acquisition
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Solution Overview
Problem
Current condition monitoring systems for steam turbines are limited in detecting component wear or damage without system shutdown, leading to significant outage costs and time, and existing methods for evaluating fatigue damage are not comprehensive for real-time monitoring.
Innovation Solution
A data acquisition system that receives operating parameters from sensors to determine run time, start-up temperature transients, and calculates fatigue severity factors, allowing for continuous monitoring and predictive maintenance by altering operating parameters or initiating repair procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection during system shutdown is used to detect component wear or damage, then detection capability is improved, but system downtime and outage costs increase
Solution Approach 1:
The monitoring system performs preliminary detection of component wear and damage during normal operation by continuously collecting and analyzing operating parameters (temperature, pressure, vibration, etc.). This allows wear detection to occur before shutdown is required, eliminating the need to wait for scheduled outages to assess component condition.
Solution Approach 2:
The patent replaces manual visual inspection with an automated electronic monitoring system that uses sensors and data processing to detect component wear. This substitution enables continuous monitoring during operation, transforming the inspection process from a shutdown-dependent mechanical task to an automated real-time electronic assessment.
2Loss of information
If traditional condition monitoring systems are used, then some component status information is obtained, but comprehensive fatigue damage assessment is not achieved
Solution Approach 1:
The system merges multiple data sources including operating parameters (temperature, pressure, flow rates), vibration data, and cycle count information into a unified monitoring platform. By combining these diverse information streams, the system achieves comprehensive fatigue damage assessment that neither traditional monitoring systems nor individual sensors could provide alone.
Solution Approach 2:
The system implements feedback mechanisms where detected wear patterns and fatigue indicators are continuously fed back to adjust monitoring thresholds and alert levels. This feedback loop enables the system to learn from accumulated data and improve its fatigue damage assessment accuracy over time, providing progressively more precise predictions.
3Productivity
If increased cycling operation is implemented to meet market demands, then productivity is improved, but fatigue damage and component wear increase
Solution Approach 1:
The system dynamically adjusts monitoring intensity and alert thresholds based on actual operating conditions and accumulated cycle counts. During periods of increased cycling, the system intensifies monitoring of critical parameters and adjusts predictions in real-time, allowing the turbine to operate at high productivity levels while continuously assessing and managing the increased fatigue risk.
Solution Approach 2:
The system changes operational parameters such as temperature limits, pressure thresholds, and cycle rate restrictions based on accumulated wear data and fatigue predictions. When the monitoring system detects that fatigue damage is approaching critical levels, it automatically recommends or implements parameter adjustments to reduce stress on components, thereby maintaining productivity while extending component lifespan.
Data Source
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AI summary
The present application provides a method of evaluating fatigue damage in a turbine (100) by a data acquisition system. The method may include the steps of receiving a number of operating parameters from a number of sensors (380), determining: a run time from start-up until the turbine reaches X percent load and a start-up temperature transient from start-up until the turbine reaches X percent load, calculating: a run time ratio of the determined run time until the turbine reaches X percent load to a predetermined run time, a start-up temperature ratio of the determined start-up temperature transient to a predetermined start-up temperature transient, and a fatigue severity factor by averaging the run time ratio and the start-up temperature ratio, and based upon the determined fatigue severity factor, altering one or more of the operating parameters and/ or initiating repair procedures.