Gas Turbine Combustor Inspection Using Abnormality Indices
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Solution Overview
Problem
Current inspection and maintenance practices for gas turbine combustors are inefficient, often leading to prolonged maintenance times and reduced economic efficiency due to incongruent determination of abnormal situations and unnecessary inspections across all combustors, which lowers operational reliability and increases costs.
Innovation Solution
A device that automatically inspects and diagnoses gas turbine combustors using a calculation based on combustion gas flow location temperature index, average combustion gas flow temperature index, gas turbine swirl angle index, and power output index, determining inspection locations through an equation that identifies abnormal conditions and prioritizes necessary maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent inspection is conducted on all combustors to ensure safety, then reliability is improved, but maintenance time and operational downtime increase
Solution Approach 1:
The patent transforms the inspection approach by changing from uniform frequent inspection to targeted inspection based on calculated abnormality indices. Multiple parameters (temperatures T1-T4, pressures P1-P4, flow rates) are measured and processed through mathematical algorithms to generate combustor-specific abnormality indices, enabling differentiation between normal and abnormal combustors for optimized inspection scheduling
Solution Approach 2:
The patent replaces manual inspection judgment with automated computational analysis. A computer executes mathematical algorithms that process sensor data and calculate abnormality indices, substituting human decision-making with objective computational results to determine which combustors require inspection
2Measurement precision
If inspection is performed on all combustors when abnormality is suspected, then detection accuracy is improved, but productivity decreases due to unnecessary inspections
Solution Approach 1:
The patent applies different inspection strategies to different combustors based on their individual abnormality indices. Instead of uniform inspection, each combustor is evaluated separately and only those exceeding abnormality thresholds are selected for inspection, making the inspection quality and resource allocation locally optimized rather than uniformly applied
Solution Approach 2:
The patent divides the combustor population into distinct groups: normal combustors (abnormality index below threshold) and abnormal combustors (abnormality index above threshold). This segmentation enables targeted inspection of only the abnormal group, separating necessary inspections from unnecessary ones to maintain detection accuracy while improving productivity
3Ease of operation
If manual determination of maintenance items is used, then flexibility is maintained, but manufacturing precision of diagnosis decreases
Solution Approach 1:
The patent implements a feedback mechanism where sensor measurements are continuously fed into computational algorithms that calculate abnormality indices. The system automatically compares calculated indices against thresholds and generates inspection recommendations, creating a closed-loop feedback system that improves diagnosis precision through objective data processing while maintaining operational flexibility through programmable parameters
Data Source
AI summary
A device capable of automatically inspecting and diagnosing an inspection location when an abnormal situation occurs to a gas turbine combustor. In an example embodiment, an inspecting and diagnosing device capable of inspecting and diagnosing inspection locations of a plurality of combustors provided in a gas turbine, the inspecting and diagnosing device calculates as result values function values for unit devices of a power generation facility using a combustion gas flow location temperature index (X), an average combustion gas flow temperature index (Y), a gas turbine swirl angle index (Z), and a gas turbine power output index (MW).