Contact life evaluation system and method based on multi-source data and neural network
A multi-source data and neural network technology, applied in the field of high-voltage circuit breaker testing, can solve the problem of single basis for contact life, achieve intuitive and accurate results, strict evaluation rules, and rich data sets
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Embodiment 1
[0044] The present invention provides a contact life evaluation system based on multi-source data and neural network, such as figure 1 As shown, the system includes a multi-source data acquisition module, a multi-source data processing module, a feature value extraction module and a learning evaluation module;
[0045] The multi-source data acquisition module collects the contact parameters in each opening process in real time, until the contacts are scrapped when the Nth opening occurs, and N sets of contact parameters are obtained, and the contact parameters are output to the The multi-source data processing module;
[0046] The multi-source data processing module obtains the dynamic contact resistance-temperature curve R(T) and the dynamic contact resistance-stroke curve R(S) of the contact according to the contact parameters, and outputs the two sets of curves to the The feature value extraction module;
[0047] The eigenvalue extraction module extracts eigenvalues in ...
Embodiment 2
[0067] This embodiment provides a contact life assessment method based on multi-source data and neural network, such as image 3 As shown, the method includes the following steps:
[0068] S1: The multi-source data acquisition module obtains the contact parameters during the opening process through the sensor element;
[0069] S2: The multi-source data processing module obtains the dynamic contact resistance-temperature curve R(T) and the dynamic contact resistance-travel curve R(S) of the contact according to the contact parameters obtained in S1;
[0070] S3: The eigenvalue extraction module extracts eigenvalues according to the two sets of curves obtained in S2;
[0071] S4: The learning evaluation module forms a data set according to the feature values obtained in S3, and uses the data set to train the neural network;
[0072] S5: Input the eigenvalues of the contacts of the circuit breaker to be tested into the trained neural network, and the neural network output...
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