Switch Life Curve Prediction Using Regression Models
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Solution Overview
Problem
Existing methods for determining the lifespan of switch types based on maximum operating current are inaccurate, leading to premature or delayed replacement, as switches often operate at various currents.
Innovation Solution
A system and method for generating a life curve of switch types by creating a switch failure dataset through controlled cycling, determining a best fit distribution using regression models, and adjusting mean failure data by confidence levels to predict the number of cycles before failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If switch lifespan is predicted based on maximum operating current, then the prediction process is simple, but the prediction accuracy is poor
Solution Approach 1:
The patent transforms the single-parameter (maximum current) prediction approach into a multi-parameter approach by incorporating actual operating current, cycle count, and statistical distribution parameters. This changes the prediction model from simple to sophisticated, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent replaces the simple mechanical assumption (lifespan proportional to maximum current) with a statistical regression model that accounts for variable operating conditions. This substitution enables accurate prediction while maintaining computational efficiency through automated data processing.
2Measurement precision
If comprehensive switch testing is performed to achieve accurate lifespan data, then prediction accuracy improves, but testing time and resources increase
Solution Approach 1:
The patent performs preliminary comprehensive testing on a sample set of switches to establish the failure dataset and regression model. Once the model is trained on this initial comprehensive data, it can predict lifespan for future switches without requiring repeated exhaustive testing, thus resolving the time-cost contradiction.
Solution Approach 2:
The patent creates a statistical model (a copy of the failure patterns) from comprehensive testing of sample switches. This model then serves as a surrogate for actual testing, allowing accurate predictions without time-consuming physical tests on every switch, resolving the contradiction between data accuracy and testing time.
Data Source
AI summary
An example system for generating a life curve associated with a switch type and an associated method. In some embodiments, the method may include generating a switch failure dataset associated with the switch type. In some embodiments, the switch failure dataset comprises mean failure data associated with the switch type indicating a predicted number of times a switch of the switch type may be cycled before failing. In some embodiments, the method may include determining a best fit distribution for the switch type based at least on the switch failure dataset using one or more regression models. In some embodiments, the method may include generating the life curve for the switch type based at least on the best fit distribution.