Switch Open-Circuit Fault Diagnosis Using Three-Phase Current Features
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Solution Overview
Problem
Existing methods for diagnosing open-circuit faults in switches require high-specification computers and prolonged training times, and are susceptible to human error in pattern classification.
Innovation Solution
A system and method using an artificial neural network model that inputs DC components and RMS values of three-phase current, with a simplified calculation equation, to diagnose open-circuit faults during regenerative motor operation, reducing the number of neurons and layers in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two artificial neural networks are used with DC components and total harmonic distortion as input values, then open-circuit fault diagnosis accuracy is improved, but computer specification requirements increase and training time is prolonged
Solution Approach 1:
The patent extracts only the essential features (DC components and RMS values of three-phase current) from the current signal, discarding redundant information such as total harmonic distortion calculations. This extraction approach reduces the dimensionality of input data from multiple harmonic components to just two key parameters, thereby simplifying the neural network architecture and reducing computational requirements while maintaining diagnostic accuracy.
Solution Approach 2:
Instead of using a complex two-network architecture that processes multiple current characteristics, the patent applies a simplified single-network approach that focuses on partial but sufficient features (DC components and RMS values). This partial action approach achieves the necessary diagnostic capability without the excessive computational overhead of processing all possible current parameters.
2Measurement precision
If two artificial neural networks are used with DC components and total harmonic distortion as input values, then open-circuit fault diagnosis accuracy is improved, but training time is prolonged
Solution Approach 1:
The patent extracts only the essential features (DC components and RMS values of three-phase current) from the current signal, discarding redundant information such as total harmonic distortion calculations. This extraction approach reduces the dimensionality of input data from multiple harmonic components to just two key parameters, thereby simplifying the neural network architecture and reducing computational requirements while maintaining diagnostic accuracy.
Solution Approach 2:
Instead of using a complex two-network architecture that processes multiple current characteristics, the patent applies a simplified single-network approach that focuses on partial but sufficient features (DC components and RMS values). This partial action approach achieves the necessary diagnostic capability without the excessive computational overhead of processing all possible current parameters.
3Ease of operation
If manual classification of current distortion patterns is performed, then fault diagnosis can be conducted, but human error susceptibility increases
Solution Approach 1:
The patent implements an automatic neural network-based diagnosis system that performs fault identification without human intervention. The system self-learns from training data and automatically classifies fault conditions based on input current parameters, eliminating the need for manual pattern recognition and thereby removing human error from the diagnostic process while maintaining ease of operation.
Data Source
AI summary
Disclosed is a system for diagnosing open-circuit faults in switches using an artificial neural network model. In the switch open-circuit fault diagnosis system, data input to an input layer of the artificial neural network model includes DC components and RMS values of three-phase current.