Mechanical working state fault classification method for port quay crane based on decision tree algorithm

Abstract

The invention discloses a mechanical working state fault classification method for port quay crane based on decision tree algorithm, which comprises the following steps: step 1, acquiring acquired data through a sensor installed on a quay crane, and storing the acquired data in a database; 2, obtaining standard fault sample data by analyzing various historical monitoring quantities of the quay crane equipment, and then carrying out classification analysis on the fault sample data by adopting a decision tree generation algorithm to obtain a fault decision tree; And step 3, classifying the acquired real-time monitoring data by using the decision tree obtained in the step 2 as a classification model of the fault mode, thereby determining the fault type. The method does not need manual data recording, data processing is directly carried out through the data collected by the sensor, the error-tolerant rate of the data is reduced, and a more accurate result is obtained. According to the method, large-scale monitoring data of the quay crane can be efficiently processed without other algorithms, and the method has universality and operability.

Description

technical field [0001] The invention relates to monitoring and fault prediction of working state data classification of quay cranes, in particular to a decision-based classification algorithm used for analysis of working state data of port machinery. Background technique [0002] At present, with the rapid development of port shipping and transportation, the use of port machinery is also growing rapidly, and the number of TEUs that need to be handled each year is huge. The speed and status of the quay crane machinery are directly related to the efficiency of the entire transportation. When hoisting containers, the movement and force of the quay crane will be affected by various factors. At this time, it is necessary to monitor every part of the quay crane and perform fault analysis, which will generate huge data information. Quickly extracting classified data becomes particularly important. To timely discover the hidden dangers of the quayside bridge. This plays an importa...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide a data classification method and system using a decision tree, in order to solve the technical problems of large workload, low efficiency and high risk of fault detection in the working status system of the quay crane in the prior art

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