A production line equipment state prediction and identification device

Abstract

The present invention relates to a production line equipment state prediction and recognition device. The present invention mainly includes a data storage subunit, a data preprocessing subunit, a data segmentation subunit, a model construction subunit, a model training subunit, a prediction processing subunit, and an auxiliary processing subunit. Subunit, loop prediction subunit, result determination subunit. Among them, the prediction processing subunit can make a preliminary prediction of the equipment state, and the auxiliary processing subunit can realize the prediction of the prediction residual of the prediction processing subunit, and can correct the preliminary prediction result, and then implement the equipment through the cyclic prediction subunit. Multi-step prediction of state data, the result determination subunit finally gives the health diagnosis result of the equipment. The production line equipment state prediction and identification device provided by the present invention makes up for the problem of gradually amplifying the prediction error when a single model performs multi-step data prediction, effectively utilizes data correlation information, and improves the multi-step prediction accuracy of data.

Description

technical field [0001] The invention relates to the technical field of production line safety, in particular to a device for predicting and identifying the state of production line equipment. Background technique [0002] With the development of science and technology, modern industry has basically realized mechanized and automated production processes. More and more large-scale mechanized equipment is introduced in the production line work. These equipments generally have a sequential production sequence relationship. As a part of the production line, a single equipment, its stability and safety directly affect the continuous operation of the entire production line. Once in the production line If the equipment in operation breaks down, its maintenance time directly affects the production efficiency of the entire production line. Therefore, how to effectively predict the operating status of the equipment in the production line is very important for improving the production ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of complex production line data and large noise interference, as well as the shortcomings and shortcomings of step-by-step amplification of errors in the current multi-step prediction of time series data, the present invention introduces a residual The processing subunit of difference prediction corrects the prediction result through the prediction residual value, which can effectively reduce the prediction error of each step

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