Fatigue Prognosis Control for Industrial Plant Components
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Solution Overview
Problem
Existing methods for predicting and managing material fatigue in large-scale industrial plants, such as power plants, are inadequate, leading to potential component failures and inefficient maintenance planning due to unpredictable operating conditions and variable load changes.
Innovation Solution
A method that determines fatigue prognosis values for plant components by analyzing current fatigue states and planned state changes, using a control system that integrates sensor data, thermodynamic simulations, and empirical knowledge to identify the leading component for maintenance needs and optimize resource-saving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fatigue monitoring and prognosis systems are implemented to predict component failures, then reliability and service life extension are improved, but device complexity and measurement precision requirements increase
Solution Approach 1:
The system segments fatigue monitoring by identifying a 'leading component' with the highest fatigue prognosis value among multiple components. This segmentation allows the complex multi-component monitoring system to be managed through focused analysis of the most critical component, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
The system performs preliminary fatigue prognosis calculations using stored thermodynamic models and empirical knowledge before actual failures occur. By predicting fatigue values in advance based on planned state changes and historical data, the system enables proactive maintenance planning, extending service life while managing complexity through advance preparation.
2Measurement precision
If detailed fatigue analysis and thermodynamic simulations are performed for each component, then manufacturing precision and measurement accuracy are improved, but productivity and ease of operation deteriorate
Solution Approach 1:
Instead of performing uniform detailed fatigue analysis on all components, the system applies local quality by concentrating precise thermodynamic simulations and fatigue calculations only on the leading component. This localized approach maintains high measurement precision where needed while improving productivity by avoiding redundant detailed analysis of less critical components.
Solution Approach 2:
The system changes parameters by using empirical knowledge and stored thermodynamic models to estimate fatigue values for non-leading components, rather than performing full detailed simulations. This parameter change from exhaustive simulation to empirical estimation maintains sufficient measurement precision while significantly improving maintenance planning efficiency.
3Adaptability or versatility
If flexible and variable operating modes are adopted to adapt to current load requirements, then adaptability is improved, but fatigue accumulation and component wear increase
Solution Approach 1:
The system implements feedback by continuously monitoring actual operating modes and comparing them with planned state changes. The fatigue prognosis values are updated based on actual measurements, allowing the system to adapt to flexible operating modes while providing feedback on their impact on component life. This enables dynamic adjustment of maintenance schedules to account for variable adaptability requirements.
Solution Approach 2:
The system embraces dynamics by accepting and measuring the effects of flexible, variable operating modes on component fatigue. Rather than imposing rigid fixed patterns, the system dynamically adjusts fatigue prognosis based on actual operating conditions, allowing adaptability while managing service life through real-time prognosis updates and informed maintenance decisions.
4Reliability
If maintenance and replacement interventions are performed early to ensure safety, then reliability is improved, but productivity and loss of time increase due to unnecessary plant downtimes
Solution Approach 1:
The system performs preliminary fatigue prognosis assessments to determine the actual remaining service life of components before scheduling maintenance. By calculating predicted fatigue values in advance based on leading component analysis, the system enables maintenance to be performed only when truly needed, avoiding unnecessary early interventions and reducing plant downtime while maintaining safety.
Solution Approach 2:
The system enables self-service by providing automated fatigue prognosis calculations and maintenance recommendations based on monitored operating data. This reduces the need for conservative early maintenance by allowing the system to self-assess its actual fatigue state and schedule maintenance optimally, balancing safety with minimized downtime.
Data Source
Figure 1
AI summary
The invention relates to a method for operating an industrial scale installation, especially a power plant installation, according to which a number of installation operating parameters characterising the operating state of the technical installation, and a number of component operating parameters respectively relevant to a number of selected components of the technical installation, are monitored and stored in a memory device (2). A fatigue characteristic value characterising the current fatigue state of each component is determined for the or each selected component as required, on the basis of the stored installation operating parameters and/or the stored associated component operating parameters. The aim of the invention is to especially develop an operation of the installation which especially protects resources. To this end, an associated fatigue prognosis value is determined from the fatigue characteristic value characterising the current fatigue state for the or each selected component, on the basis of guiding parameters characterising a planned change of state.