Fault Prediction Of Logistics Equipment Based On Digital Twin

As an important tool for enterprise production and transportation,the reliability and fault handling ability of logistics equipment are directly related to the transportation capacity and transportation efficiency of the enterprise.With the automation and intelligence of the logistics system,the stable operation of modern logistics equipment has become a key link in enterprise management,and the lack of efficient equipment maintenance and management will lead to frequent system failures,low operating efficiency,and huge cost consumption.First of all,the paper discusses the research background and significance of predictive maintenance of logistics equipment based on the digital twin technology that has attracted much attention at present,and consults the latest research results at home and abroad,including digital twin technology and equipment failure prediction theory,and summarizes the research methods and Paper framework.Secondly,the related theories of logistics equipment and predictive maintenance are discussed,including the time domain analysis and frequency domain analysis theories of vibration signals.Then,the fault prediction mode and equipment failure prediction model based on digital twin technology are constructed.Finally,a pull-type equipment maintenance system based on the current situation of G company is constructed to realize the transition from preventive maintenance to predictive maintenance of enterprise equipment.(1)Based on the digital twin technology,construct the logistics equipment fault prediction model,construct the equipment prediction acquisition framework and the equipment digital twin;and use the Lab VIEW software to establish the equipment vibration signal acquisition and analysis system to realize the equipment status signal acquisition and analysis.(2)The Marine Predators Algorithm(MPA)was used to optimize the Support Vector Machine(SVM)to build an equipment failure prediction model.Based on the rolling bearing laboratory data of Western Reserve University,from three different eigenvalue perspectives The prediction accuracy of the validation model is high.In addition,based on the eigenvalue data in the time-frequency domain,the fault prediction model of MPA-SVM is compared with the two fault prediction models of PSO-SVM and SVM,and it is concluded that this model has obvious advantages.(3)Taking the vulnerable parts-rolling bearing in the hoist of G company’s logistics storage equipment as an example,build a high-simulation behavior process of equipment based on digital twins,and use the equipment operation data actually collected by the enterprise to verify the equipment failure prediction model again.,indicating that the model has very good practical application value.In addition,based on the current situation of G company,the pull-type equipment maintenance process and equipment maintenance management standardization are proposed to realize the efficient operation of the equipment.