Automated Turbine Maintenance Financial Reporting System
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Solution Overview
Problem
Current methods for generating turbine maintenance schedules in power plants are time-consuming and costly, relying on manual calculations and condition-based predictions, which can lead to incorrect part replacement and unanticipated shutdowns due to errors in data entry and complex algorithms.
Innovation Solution
A method and system that automatically generate a financial report for turbine maintenance events by analyzing operational history, inspection data, replacement part costs, repair costs, and vendor service costs, using a database and network-based system to provide a pre-determined operational forecast and schedule maintenance events and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual calculations and condition-based prediction are used to generate maintenance schedules, then the system can provide customized maintenance planning, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service by automatically collecting operational data from plant systems and generating maintenance schedules without requiring manual data entry. The automated algorithm processes operational hours, part lifecycles, and maintenance criteria to produce maintenance schedules independently, eliminating the time-consuming manual calculation process while maintaining customized planning capabilities.
Solution Approach 2:
The patent replaces the mechanical manual calculation process with an automated computational system. Instead of operators manually entering data and performing calculations, the system uses automated algorithms to process operational data, calculate part lifecycles, and generate maintenance schedules, significantly reducing time consumption and human error.
2Adaptability or versatility
If manual data entry is used to input inspection data into the algorithm, then the system can process condition-based information, but errors increase and reliability decreases
Solution Approach 1:
The system automatically collects operational data directly from plant monitoring systems and inspection databases, eliminating manual data entry. This self-service approach ensures data accuracy by reducing human intervention while maintaining the system's ability to process condition-based information for maintenance predictions.
Solution Approach 2:
The system incorporates feedback mechanisms where operational data from plant systems continuously updates the maintenance scheduling algorithm. This feedback loop ensures that the most current operational hours, part conditions, and lifecycle data are used for generating accurate maintenance schedules, improving reliability through continuous data validation and updates.
3Reliability
If complex algorithms are used to determine operating conditions and part lifecycles, then the maintenance planning can be comprehensive, but the computational complexity and cost increase
Solution Approach 1:
The complex maintenance planning algorithm is segmented into distinct functional modules: operational data collection, part lifecycle calculation, maintenance criteria evaluation, and schedule generation. Each module handles a specific aspect of the planning process independently, reducing overall system complexity while maintaining comprehensive maintenance planning capabilities through modular design.
Data Source
AI summary
A method for generating a financial report indicative of estimated turbine maintenance costs is provided. The method includes receiving, at a database, turbine operational history data and turbine inspection data from a user for a pre-identified turbine, receiving, at the database, turbine replacement part costs, turbine part repair costs, and vendor service costs associated with the pre-identified turbine, inputting a pre-determined turbine operational forecast into the database, analyzing turbine maintenance information including at least one of turbine operational history data, turbine inspection data, replacement part costs, part repair costs, vendor service costs, and turbine operational forecast, and automatically generating a financial report including at least one schedule of turbine maintenance events and costs associated with each event based on the turbine maintenance information analysis.