Recognition method for operating state of industrial equipment and server

Abstract

The embodiment of the invention discloses a recognition method for the operating state of industrial equipment and a server. The method includes the steps that the server acquires a first training set; if the server inputs the first training set into a selected first target model, the server uses a first target algorithm to train the first target model , and a second target model is obtained; if the server determines that the accuracy of the second target model does not reach a preset accuracy threshold, the server adjusts hyper-parameters of the second target model according to a preset rule;if the server inputs the first training set into the second target model with the hyper-parameters adjusted, the server uses the first target algorithm to train the second target model with the parameters adjusted, and a third target model is obtained; the server judges whether or not the accuracy of the third target model reaches the accuracy threshold, wherein the accuracy of the third target model is obtained by the server according to a first test set; if yes, the server uses the third target model to predict the operating state of the industrial equipment.

Description

technical field [0001] The embodiments of the present application relate to the field of information technology, and in particular, to a method and server for identifying the operating status of industrial equipment. Background technique [0002] Modern industrial equipment is increasingly large, sophisticated and complex, which puts forward higher requirements for the stability and reliability of industrial equipment. However, due to factors such as the complex operating environment of industrial equipment and the gradual degradation of component performance over time, equipment failures are inevitable and random. Effective fault prediction for industrial equipment can predict possible problems of the equipment, notify the staff in advance to repair and deal with it, and prevent minor faults from turning into serious faults; it can also avoid unnecessary maintenance work according to the maintenance cycle and reduce maintenance expenditure. [0003] With the popularization...

AI Technical Summary

Problems solved by technology

[0005] In the existing industrial equipment failure prediction methods, if the accuracy of the trained model does not reach the preset accuracy, developers need to manually adjust the parameters of the trained model or re-select another mathematical model to re-establish a The operation process of the model to be trained is relatively cumbersome

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