Controller Generating Maintenance Packages for Power Plant Turbines
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Solution Overview
Problem
Power plant maintenance operations often require shutdowns, leading to decreased production and revenue due to inefficiencies in scheduling and planning.
Innovation Solution
A system and method utilizing a controller with a processor to generate maintenance packages based on sensor data, user input, and life odometer and condition monitoring solutions, which include scheduled maintenance activities and recommendations to minimize downtime and optimize component lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance operations are performed on the power plant system, then component reliability is improved, but production and revenue decrease due to shutdowns
Solution Approach 1:
The system performs preliminary actions by continuously monitoring component conditions and predicting failures before they occur. The life odometer and condition monitoring solutions enable the system to schedule maintenance during planned outages rather than responding to unexpected failures, thereby improving reliability while minimizing unplanned production losses.
Solution Approach 2:
The system implements feedback mechanisms through continuous sensor monitoring and condition assessment. The controller receives real-time data from sensors, analyzes component health status, and adjusts maintenance scheduling accordingly. This closed-loop feedback enables optimized maintenance timing that balances reliability requirements with production needs.
2Reliability
If maintenance operations are performed on the power plant system, then component reliability is improved, but time and efficiency are lost due to shutdowns
Solution Approach 1:
The system performs preliminary condition assessment and failure prediction before maintenance is needed. By continuously monitoring component life and conditions, the system prepares maintenance schedules in advance during planned outages, reducing the actual time required for maintenance operations and minimizing overall downtime.
Solution Approach 2:
The system dynamically adjusts maintenance schedules based on real-time component conditions rather than following fixed time-based intervals. The controller continuously updates maintenance timing based on actual component wear and operational conditions, optimizing the balance between reliability and time loss.
3Reliability
If traditional maintenance scheduling is used, then maintenance coverage is ensured, but efficiency and productivity decrease due to poor planning
Solution Approach 1:
The system uses continuous feedback from sensor data and condition monitoring to optimize maintenance scheduling. The controller analyzes real-time component status and adjusts maintenance plans dynamically, ensuring adequate maintenance coverage while improving efficiency through data-driven decision-making and reduced unplanned outages.
Solution Approach 2:
The system changes the parameters of maintenance scheduling from fixed time-based intervals to condition-based timing. By monitoring component life odometer readings and condition parameters, the system optimizes maintenance timing to coincide with planned outages, thereby maintaining reliability coverage while significantly improving operational efficiency.
Data Source
AI summary
A power plant system includes a turbine system, a sensor, and a controller. The sensor is configured to collect a first set of data regarding the turbine system. The controller is configured to receive user input regarding constraints of the turbine system, a second set of data regarding the power plant system, and the first set of data. The controller is configured to determine whether a first notification is present based on a determined status and to generate a first maintenance package based on the first notification, life odometer solutions, condition monitoring solutions, and the first set of data. The controller is configured to generate a model of implementing the first maintenance package with respect to the turbine system as well as to generate a second maintenance package based on the user input, the second set of data, the first maintenance package, and the scenario model.