Power Generation Risk Model Combining Historical and Real-Time Data
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Solution Overview
Problem
Current methods for assessing the risk of unplanned outages in power generation units are inadequate, as they rely on historical data and do not accurately account for unforeseen failure modes, leading to potential operational failures and reduced power generation efficiency.
Innovation Solution
A semi-empirical model-based approach that combines historical field failure data with real-time monitoring insights from boroscopic inspections to calculate the risk of unplanned outages, using three empirical models to predict failures based on operational conditions, wear, and degradation, and expert opinion to account for unknown failure modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the period between successive off-line repair and maintenance events is extended to increase power generation, then the total amount of power generated increases, but the risk of unplanned outage increases
Solution Approach 1:
The system performs preliminary risk assessment and monitoring before failures occur. By continuously evaluating operational conditions, wear patterns, and degradation trends using multiple models (historical data model, real-time monitoring model, and lurking model), the system predicts potential failures in advance and schedules maintenance proactively, preventing unplanned outages while maximizing operational periods
Solution Approach 2:
The system implements continuous feedback loops through real-time monitoring of operational parameters, wear indicators, and degradation patterns. This feedback informs dynamic adjustment of maintenance schedules and risk assessments, allowing the system to optimize the balance between extended operation and failure prevention by adapting to actual machine conditions rather than relying solely on fixed schedules or historical averages
2Device complexity
If traditional historical data methods are used to assess failure risk, then the assessment process is simple, but the accuracy of failure risk prediction is insufficient
Solution Approach 1:
The system merges three distinct modeling approaches into a unified risk assessment framework: historical failure data modeling, real-time monitoring and inspection data modeling, and lurking model for unseen failure modes. This combination integrates diverse data sources and analytical methods to comprehensively assess failure risk, achieving high prediction accuracy by capturing both known and potential failure mechanisms that any single method would miss alone
Data Source
AI summary
A method to determine a risk of failure for a machine including: generating a first value for a risk of failure of the machine, wherein the first value is determined by a first model receiving as an input a condition of the machine and the first model includes a relationship derived from historical machine failures and correlating the input condition of the machine to a value for the risk of failure; generating a second value of the risk of failure of the machine, wherein the second value is determined by a second model receiving as an input information regarding wear or degradation of the machine and the second model includes a relationship correlating the input information regarding wear or degradation to a value for the risk of failure, and determining a total risk of failure based on the first and second values of the risk of failure.