Research On Intelligent Fault Diagnosis Of Mining Truck Drive System Based On Data Drive

Intelligent coal mine technology is the core technology to realize the safe and efficient mining of national energy.As an important mining and transportation equipment for coal mines,the failure of important parts of mining trucks may affect production efficiency and even affect coal mining safety.Therefore,the study of advanced and intelligent fault diagnosis technology for mining trucks has very important research significance for ensuring the safe and efficient operation of coal mine production and transportation.The driving system is the key component of the mining truck.This article takes the mining truck driving system as the research object and adopts a data-driven approach to realize intelligent fault diagnosis.First,the structure and failure conditions of the mining truck drive system are analyzed,and the main failure modes,vibration mechanism and vibration characteristic frequencies of bearings and gears are analyzed respectively,which lays a theoretical foundation for feature extraction and intelligent fault diagnosis.Secondly,the wavelet packet is used for denoising simulation analysis and further realize the noise reduction processing of the vibration signal of the driving system of the mining truck;although the empirical mode decomposition can effectively decompose the signal,it has end effects and modal aliasing problems,so the collective empirical model State decomposition decomposes the vibration signal,calculates the energy ratio of the six eigenmode functions that best represent the state characteristics,and uses it as a fault feature vector to achieve fault feature extraction.The fault pattern recognition algorithm based on support vector machine is studied again.In order to improve the accuracy of fault state recognition,particle swarm optimization,genetic algorithm and multi-population genetic algorithm are introduced to optimize the parameters of the penalty factor C and the kernel function parameter g of the support vector machine.Through the comparison of the results of the four models,it is concluded that the support vector machine model optimized based on the multiple population genetic algorithm has the highest accuracy and the best performance.Thus,an intelligent fault diagnosis of the mining truck driving system based on the multiple population genetic algorithm optimization support vector machine is constructed.model.Finally,this article aims at the domestic self-developed XDE320 electric drive mining dump truck drive system.Through hardware equipment such as sensors,collectors,computers,and software such as Lab VIEW and MATLAB,a set of data acquisition and storage modules,time-frequency analysis modules,The feature extraction module and the intelligent fault diagnosis module are the four modules of the mining truck intelligent fault diagnosis system,which performs time-frequency analysis on the collected vibration data,and realizes the status monitoring and real-time response of the mining truck driving system;The fault data of the mining card is insufficient,so the fault feature extraction and intelligent diagnosis of the signal through the existing bearing and gear fault data set verify the practicability and reliability of the designed intelligent fault diagnosis system;as the mining truck runs The accumulation of data enables the system to accurately reflect the status of the equipment and intelligently diagnose the fault location,improve the stability of the equipment and the timeliness of maintenance,and ensure the safe and efficient production of coal.There are Figure 67,Table 18,and 102 references in the paper.