Method and system for predicting service life of electromechanical equipment based on data driving

Abstract

The invention relates to the technical field of electromechanical equipment life prediction, in particular to an electromechanical equipment life prediction method and system based on data driving, and the method comprises the steps: obtaining a data set of multiple pieces of equipment; abnormal data are screened out; performing period division according to the distribution of the abnormal data on the time axis; performing cycle combination on cycle division results of different devices to obtain a training set and a test set; inputting the training set into a selected data driving model for training; inputting the test set into the trained data driving model for testing; and after the test is finished, predicting the service life of any electromechanical equipment according to the obtained data driving model. According to the method provided by the invention, the abnormal data are screened, the data of multiple devices are subjected to cycle division and then are recombined to obtain the training set and the test set, the model is trained and tested, the life prediction is completed, and the data are integrated in the data division stage, so that the model is more comprehensive and accurate.

Description

technical field [0001] The invention relates to the technical field of life prediction of electromechanical equipment, in particular to a data-driven life prediction method and system of electromechanical equipment. Background technique [0002] Data-driven is a data processing and analysis method relative to model-driven. The so-called model-driven refers to constructing a model, using the collected data, and analyzing the object according to the constructed model. This method depends on the accuracy of the constructed model. In contrast, under the data-driven analysis method, instead of constructing a fixed model, the collected data is used to train the model to fit the model to the data. [0003] Data-driven analysis methods have a higher dependence on data information, but are also more adaptable to data. In some data analysis with obvious trends and regular fluctuations, data-driven analysis methods have obvious advantages. [0004] Life prediction of electromechanica...

AI Technical Summary

Problems solved by technology

The existing technology provides some analysis methods based on data-driven electromechanical life. These methods mainly start from the perspective of how to mine data information, such as increasing the depth of the model, using different feature extraction methods, etc. Then, the data-driven analysis method itself One of the problems is that the dependence on the data is too high. How to make the data random and contain as many actual situations as possible is a problem that needs to be solved in data analysis

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