Steel wire rope surface damage intelligent monitoring method and system based on LBP features

Abstract

The invention discloses a steel wire rope surface damage intelligent monitoring method and system based on LBP features, and the method comprises the steps: firstly, building a healthy, broken and worn image data set of the surface of a steel wire rope, determining parameters of a filter, an LBP operator, a PCA dimension reduction dimension, a machine learning algorithm, and parameters of the machine learning algorithm, and obtaining an integrated algorithm model. The invention also discloses a steel wire rope surface damage intelligent monitoring system based on the LBP characteristics, and the system comprises an offline training and testing module which is used for obtaining a trained integrated algorithm model; an on-site image acquisition module used for acquiring on-site images in real time and acquiring position information of the images; and an on-line monitoring module used for carrying an integrated algorithm model to process the acquired on-site images in real time so as todiagnose the surface state of the steel wire rope in real time. According to the technical scheme, the surface damage of the steel wire rope can be efficiently detected and diagnosed.

Description

technical field [0001] The invention belongs to the field of state detection and intelligent diagnosis of mechanical parts, and in particular relates to an intelligent monitoring method and system for surface damage of steel wire ropes based on image processing and machine learning. Background technique [0002] With the development of human society, wire ropes are widely used in many fields, such as hoisting (mine hoist, elevator, crane, etc.), traction (winch, cableway, etc.), bearing (stayed cable), etc. Periodic or real-time inspection of wire rope damage is the premise to ensure the safety of wire rope. However, the traditional damage detection method uses artificial visual inspection, which has problems such as low detection efficiency, time-consuming, insufficient and unreliable detection. Therefore, it is necessary to develop non-destructive, rapid and automatic detection methods. [0003] For the non-destructive testing technology of steel wire rope, such as machi...

AI Technical Summary

Problems solved by technology

[0004] There have been many researches on the damage detection method based on machine vision in the field of material surface detection, but there are few automatic vision methods for steel wire rope inspection, and these methods lack consideration of the impact of illumination, feature dimension, and performance of different machine learning algorithms

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