Fault prediction method, terminal and readable storage medium based on elevator operation parameters

Abstract

The invention provides a fault prediction method, terminal and readable storage medium based on elevator operation parameters, the method is used for acquiring elevator parameters which are sensitiveto elevator faults in the elevator data, an elevator parameter set is formed, data processing is carried out on the elevator parameter set, abnormal values of parameters in the elevator parameter setare removed, adopting an interpolation method to fill out missing data of the elevator parameters, calling an elevator parameter set Q of M time points as a training data set, the same mechanism of the M-time elevator serves as a N-dimensional elevator parameter vector, and the elevator parameters are processed. The method comprises the following steps of constructing a model of a multilayer convolution neural network; performing fault prediction on the elevator parameters, the method is based on fault prediction of operation parameters of the elevator, the current acquired elevator parameterscan be utilized, prediction can be carried out on the basis of previous elevator parameters, a perfect elevator fault prediction model is built, and fault prediction is carried out.

Description

technical field [0001] The invention relates to the technical field of elevators, in particular to a fault prediction method based on elevator operating parameters, a terminal and a readable storage medium. Background technique [0002] With the increase of high-rise buildings, the use of elevators is relatively large. Elevators are generally installed in buildings, and elevator cars can be used for people to carry or load goods. Generally, the elevator car runs vertically on the horizontal plane, or moves up and down on a rigid track with an inclination angle of less than 15° from the plumb line, which brings convenience to people going up and down the stairs. [0003] At present, the elevator is also responsible for the safety of personnel. Therefore, the elevator needs to be maintained and protected every time it runs for a period of time, to determine whether there is a hidden trouble, and to avoid accidents as much as possible. The current method used is that some ter...

AI Technical Summary

Problems solved by technology

This method cannot carry out data synthesis to predict the risks and hidden dangers of elevators

It is not possible to judge the fault trend based on the current state of not exceeding the threshold, so that the fault information can only be obtained when the fault occurs, and there is a certain hysteresis

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