RMG rail-biting fault diagnosis method based on multi-sensor data amalgamation technology

Abstract

The invention belongs to the field of rail type hoisting equipment and rail locomotives, and relates to data processing and rail biting fault diagnosis technologies. According to the principle that various causes of rail biting can be explained by following a certain pattern, a range acquisition sensor, a gray bus, a rotary encoder and other sensors are utilized for obtaining the information about rail biting; a list of possible causes for rail biting can be formulated following a series of operations such as the pretreatment, the multi-sensor data integration technology, the fault diagnosis logical operation and the like; and the case of RMG biting rail is displayed in the form of graphics, or the causes for rail biting are reported to police in writing. The method finds the root causes of rail biting, thereby not only greatly reducing or eliminating the phenomenon of rail biting but also helping maintenance personnel master the condition of the equipment. The integration of the multi-sensor data enables the technology to be particularly applicable to environments with a harsh condition, a myriad of interference signals and the like.

Description

technical field [0001] The invention belongs to the field of rail-type hoisting equipment and rail locomotives, and relates to data processing and rail-gnawing fault diagnosis technologies. Background technique [0002] The rapid growth of the world economy and international trade urgently requires efficient and intelligent container terminal loading and unloading to improve operational efficiency. And RMG (Rail-Mounted Gantry rail-mounted container gantry crane) is playing an increasingly important role in the loading and unloading process of container terminals because of its superior performance. pivotal role. As we all know, the phenomenon of rail gnawing is a chronic problem of cranes, and it is also a bottleneck problem restricting the development of full automation of cranes. It greatly affects the safety and work efficiency of the crane, and also increases the operation and maintenance costs of the crane. The development of cranes has a history of nearly 50 years ...

AI Technical Summary

Problems solved by technology

[0003] At present, there are many treatment methods for RMG rail gnawing at home and abroad. Most of the methods are based on real-time measurement of crane deflection-related data after rail gnawing occurs, and control the speed of both ends or one end of the cart according to a certain control algorithm. To adjust the gap between the wheel rim and the track to keep it within the normal range, the essential cause of rail gnawing cannot be found, so in the long run it is impossible to improve the working efficiency and service life of the entire system, as shown in the literature [ 1]-[4]

In some domestic research results, when certain correction measures are taken, there are also cases where the cause of correction is determined through sensor information and experience [5], but there are a series of disadvantages in its fault diagnosis system, such as few types of sensors: there is only one kind of sensor, the proximity switch, due to The working environment of cranes is generally harsh. When a sensor fails or is severely disturbed, the entire fault diagnosis system cannot continue to work; the amount of sensor information is small: only take the rail gnawing signals of the two wheels in front of the running, and pass the two Judging the rail gnawing status of other wheels by a single rail gnawing signal can cause many wrong judgment results; no systematic analysis method: only based on experience, the analysis method is too simple and incomplete; the fault alarm information is vague: only the same side and / Or the phenomenon of wheel gnawing in the diagonal direction is alarmed, and experienced staff are required to analyze the alarm information and locate the cause of gnawing rails, etc.

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