Aiming at the onboard equipment with repeated failures in actual field records,a fault time prediction method based on ridge regression algorithm is proposed in order to predict its next failure time.This paper extracts six effective fault features by random forest algorithm combined with human experience in the whole research process,and establishes ridge regression fault prediction model,optimizes the hyper-parameters of the model by grid searching combined with cross validation(Gridsearch CV),which is effectively verified in actual data of a subway line in Shenzhen.The next fault can be predicted more accurately according to the proposed methods as a basis for guiding preventive maintenance,so as to realize the prediction in advance,perception in advance and processing in advance of the faults.
subway onboard equipmentfault time predictionridge regression algorithmgrid search and cross validation(Gridsearch CV)
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