Power Plant Service Menu by Operation Pattern for Predictive Maintenance
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Solution Overview
Problem
Existing methods for inspecting and maintaining gas turbines in power generation plants often fail to detect unexpected deterioration or damage, leading to delays in arranging replacement parts during periodic inspections, as they primarily rely on operation time and start-up counts without considering other operational patterns.
Innovation Solution
A service menu presentation system that acquires operation data, determines the dominant operation pattern within a certain period, and outputs a service menu tailored to this pattern, including predictive analytics to anticipate future operations and potential issues, thereby identifying locations prone to deterioration or damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If inspection items are selected based on operation time and start-up counts, then the inspection process is simple and standardized, but unexpected deterioration or damage may be missed that is not related to these parameters
Solution Approach 1:
The inspection system segments the determination of inspection items into two independent parts: (1) basic items determined by operation time and start-up counts, and (2) additional items determined by operation patterns. This segmentation allows the system to maintain the simplicity of standardized inspection while adding pattern-based detection to improve reliability and catch unexpected deterioration.
Solution Approach 2:
The system adds a new dimension to inspection item selection by introducing operation pattern analysis. Instead of relying solely on operation time and start-up counts, the system now considers the temporal patterns and sequences of operations, creating a multi-dimensional approach that improves detection accuracy without completely redesigning the inspection process.
2Reliability
If unexpected deterioration or damage is found during periodic inspection, then comprehensive inspection coverage is achieved, but it takes time to arrange replacement parts and perform replacement
Solution Approach 1:
The system performs preliminary analysis of operation patterns to predict potential deterioration before it occurs. By identifying unusual operation patterns that precede failures, the system can alert operators in advance, allowing them to prepare replacement parts and schedule maintenance during planned downtime rather than experiencing unexpected failures that cause unplanned delays.
Solution Approach 2:
The system continuously monitors operation patterns and provides feedback about abnormal conditions that may indicate impending deterioration. This feedback mechanism allows operators to take preventive action before actual failures occur, reducing the need for emergency part replacement and minimizing operational disruptions.
3Measurement precision
If multiple operation patterns are analyzed to determine inspection items, then detection accuracy improves, but the complexity of the inspection system increases
Solution Approach 1:
The system dynamically adjusts the weight and importance of different operation patterns based on their relevance to specific equipment and failure modes. Rather than analyzing all possible patterns with equal complexity, the system adapts the analysis depth and scope based on the particular application, maintaining high detection accuracy while managing system complexity through intelligent prioritization.
Data Source
AI summary
Provided is a service menu presentation system which: acquires operation data relating to an electric power generation plant; decides which operation pattern, from among a plurality of operation patterns set in advance in accordance with an output mode, is the basis for the operation being indicated by the operation data; calculates, on the basis of the operation data acquired in a certain period, an operation time with respect to each of the operation patterns in the period; determines, on the basis of an operation time ratio in the certain period, an in-period operation pattern of the certain period; and outputs a service menu corresponding to the in-period operation pattern that has been determined.