Ship unloader association rule fault prediction model method based on deep belief network

Abstract

A ship unloader association rule fault prediction model method based on a deep belief network comprises the following steps: (1) taking a time sequence of state monitoring of a bridge type grab ship unloader trolley system as model input, and obtaining associated internal feature information and fault categories of the trolley system through sampling and taking the associated internal feature information and fault categories as model output; (2) preprocessing the original monitoring data; (3) carrying out phase space reconstruction on the obtained time sequence, converting the time sequence into a matrix form, and constructing an input and output mapping relationship; (4) training a deep belief network prediction model formed by RBM stacking and a regression layer, and obtaining a future residual error sequence prediction value of which the vibration intensity of each related monitoring point is a time sequence; (5) constructing a fault type and corresponding monitoring point residualerror sequence characteristic constraint function model by utilizing an association rule; and (6) inputting a data set, and predicting by using the trained model. The method is high in precision, simple in calculation and high in engineering practicability.

Description

technical field [0001] The present invention designs a fault prediction method for the trolley system of a bridge-type grab ship unloader, especially a fault prediction model method for a ship unloader association rule based on a deep belief network. Background technique [0002] As the main working system of the bridge grab ship unloader, the trolley operation system undertakes key functions such as lifting and opening and closing of the grab and trolley walking during the normal operation of the ship unloader, and occupies a very important position in its actual production. Failure of the trolley running system will affect the function of the whole machine, which may cause serious accidents and economic losses. Therefore, it is necessary to monitor, analyze and predict the health status of the trolley operation system to ensure the safe and stable operation of the ship unloader, early warning, reduce economic losses caused by sudden failures, and avoid major accidents such...

AI Technical Summary

Problems solved by technology

Failure of the trolley running system will affect the function of the whole machine, which may cause serious accidents and economic losses

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