Selective wear-based equipment optimal maintenance time prediction method

Abstract

The invention belongs to the field of equipment maintenance time prediction, and relates to a selective wear-based equipment optimal maintenance time prediction method. The method mainly comprises two steps of: solving a selective wear possibility value of each part of equipment in the current state by utilizing an association rule algorithm; and taking the solved possibility value as input, and solving the optimal maintenance time by neural network modeling. The method comprises the following steps of: constructing an association rule library; acquiring state monitoring data, extracting characteristic values from the data, and establishing an equipment monitoring data set; matching the equipment monitoring data set with the association rule library, and calculating the wear possibility value of each part under the condition of successful matching; and training a self-organizing competitive neural network model, and predicting the optical maintenance time by utilizing the model.

Description

technical field [0001] The invention belongs to the field of equipment maintenance time prediction, and relates to a method for predicting optimal maintenance time of equipment based on selective wear. Background technique [0002] Modern production equipment is becoming larger and more complex, and the phenomenon of equipment failure is also increasing, and the losses caused are also increasing. With the continuous popularization of computers, most of the key equipment adopts the condition-based maintenance method instead of the planned maintenance method, and the prediction of the optimal maintenance time of equipment is one of the most important problems in condition-based maintenance. In condition-based maintenance, the prediction of the optimal maintenance time of equipment mainly depends on the actual monitoring data. If the predicted optimal maintenance time is too early, it will cause economic losses due to problems such as downtime for maintenance and premature repl...

AI Technical Summary

Problems solved by technology

[0003] 1. Various information detection methods and prediction methods fail to regard the diagnostic object as an organic whole, and fail to effectively consider the possible interrelationships and influences among the various components of the equipment

[0004] 2. It is difficult to deal with complex situations where multiple faults coexist

[0005] In the actual evolution of equipment faults, there is a close relationship between the various components of the system, and various faults often occur at the same time, so it is still difficult to obtain more accurate prediction values ​​with existing technical methods

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