Residual Lifetime Prediction for Sensor-Monitored Electrical Components
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Solution Overview
Problem
Electrical systems in power plants, such as switchgear, face challenges in predicting residual lifetime due to variable thermo-mechanical and dielectric loads, leading to costly and time-consuming maintenance, with existing methods relying heavily on destructive tests and incomplete data, resulting in high costs and potential safety risks.
Innovation Solution
An apparatus and method utilizing sensors to continuously measure physical parameters like temperature, humidity, and current, assigning data to classes, and applying aging models like Palmgren-Miner, Arrhenius, and Coffin-Manson to calculate a real-time residual lifetime prediction, enabling accurate maintenance planning and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scheduled maintenance is performed at regular intervals to ensure safety, then reliability is improved, but productivity deteriorates due to expensive scheduled downtime
Solution Approach 1:
The maintenance schedule transitions from static (fixed intervals) to dynamic (condition-based). The system continuously monitors actual system conditions and adjusts maintenance timing dynamically, performing maintenance only when predicted residual lifetime indicates it is necessary, thereby eliminating unnecessary scheduled downtime while maintaining safety
Solution Approach 2:
The system implements feedback through continuous monitoring of physical parameters and iterative updating of the predicted residual lifetime. This feedback loop allows the maintenance schedule to be adjusted based on actual system degradation, enabling productivity improvement while maintaining reliability through data-driven decisions
2Measurement precision
If thorough inspections are performed at regular intervals to predict residual lifetime, then measurement precision is improved, but loss of time and productivity worsen
Solution Approach 1:
The system implements continuous monitoring of physical parameters between inspections, eliminating the need for periodic shutdowns for thorough inspections. Sensors continuously collect data on temperature, humidity, current, and other parameters, providing ongoing measurement precision without time loss
Solution Approach 2:
The system introduces sensors and processing units as intermediaries to continuously measure and analyze system conditions. These intermediaries provide accurate residual lifetime prediction data without requiring direct human inspection or system shutdown, thereby improving measurement precision while eliminating inspection time loss
3Measurement precision
If dedicated tests are performed on switchgear components to obtain reliability information, then measurement precision is improved, but productivity worsens due to being very time consuming
Solution Approach 1:
The switchgear system performs self-diagnosis through continuously monitored physical parameters. The system automatically tracks its own degradation state using sensors and processing units, eliminating the need for external dedicated tests while maintaining high measurement precision for reliability assessment
Solution Approach 2:
The system replaces physical dedicated tests with electronic sensing and computational analysis. Instead of performing mechanical or electrical tests on switchgear components, the system uses sensors to continuously measure physical parameters and processes this data to obtain reliability information, dramatically reducing time consumption while maintaining accuracy
4Reliability
If large safety margins are applied in device design to ensure reliability, then reliability is improved, but loss of substance and cost worsen
Solution Approach 1:
The safety margin transitions from a static design parameter to a dynamic operational parameter. Instead of designing with fixed large safety margins, the system dynamically adjusts the effective safety margin based on real-time condition monitoring and predicted residual lifetime, allowing smaller initial margins while maintaining reliability through adaptive management
Solution Approach 2:
The system changes the parameter of safety margin from a constant design value to a variable operational value. By continuously updating the predicted residual lifetime based on monitored physical parameters, the system effectively adjusts the safety margin throughout the component's life, reducing material costs while maintaining reliability
Data Source
Figure 1~3f
Figure 4
AI summary
The present invention relates to an apparatus (10) for prediction of the residual lifetime of an electrical system: The apparatus has an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide at least one sensor data from at least one sensor to the processing unit. The at least one the sensor data comprises the measurement of at least one physical parameter of at least one component of an electrical system. Each sensor data is associated with a corresponding sensor and relates to the measurement of one physical parameter of a corresponding component of the electrical system. Each sensor data extends over a plurality of time windows and is assigned to a certain data class, such that sensor data at a particular time window has a value that falls into one of the data classes defined by being between a minimum and maximum value defined for the respective data class. For each sensor data the processing unit is configured to assign the sensor data at each time window into a corresponding measurement window on the basis that sensor data at a particular time window has a value that falls into that corresponding data class. For each physical parameter of a component the processing unit is configured to determine a load spectrum, wherein the load spectrum is the sum of all data classes including the tata values and duration in each data class for the corresponding physical parameter of a component. The processing unit is configured to determine a predicted lifetime for the component through application of an aging model from at least one aging model to the corresponding load spectrum for each physical parameter of a component. The processing unit is configured to determine a predicted lifetime for each component of the at least one component.