Research on fault diagnosis method of boom roadheader oil-liquid system
Liu Guopeng
Abstract：The stability and reliability of the oil-liquid system of boom roadheader is crucial to the safety and efficiency of the whole e-quipment during long-term operation.The traditional single-sensor monitoring method has the limitations of limited data dimension and insufficient complementarity,which cannot comprehensively reflect the real state of the oil-liquid system in operation.In order to solve this problem,a multi-sensor oil-liquid system fault diagnosis method for roadheader based on Convolutional Neural Networks(CNN)was proposed.Firstly,lubricant monitoring and data acquisition were carried out.This step focuses on the physical and chemical performance indicators of the lubricant,such as viscosity and acid value,and the information of abrasive particles earried in the oil-liquid.Second,a CNN model was constructed and trained for fault diagnosis using the data collected from multiple sensors as input.Finally,in order to verify the effectiveness of the proposed method,it was compared with other machine learning methods in terms of the accuracy of fault state classification.The experimental results show that the proposed method is significantly higher than other traditional machine learning methods in terms of diagnostic accuracy.This result not only proves the advantages of the CNN model in dealing with such complex da-ta,but also further validates the efficiency and accuracy of the method.
Keywords：boom roadheaderoil-liquidconvolutional neural networkphysical and chemical indicatorsgrain information
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 46-52 )