Research On Life Prediction And Maintenance Decision Of Typical Parts Of Mining And Metallurgical Equipment

With the continuous development of industrial mechanization,various types of machinery and equipment have been widely used.In order to reduce costs and increase efficiency,the health management of machinery and equipment has become a major problem faced by social enterprises,and it is also a hot topic for scholars at home and abroad.In mining enterprise engineering practice activities,whether or not the mining and metallurgy equipment is operating healthily has a significant impact on the economic benefits of mining enterprises.In the actual engineering practice,due to the complexity of the working environment of the mining and metallurgy equipment and the particularity of the working conditions,the realization of the health management of the typical parts and components of the mining and metallurgy equipment faces some corresponding obstacles.The first is the purification of the collected vibration signal data.Since the prediction of the remaining working life of components requires undisturbed signal data,it is also one of the key points in the health management of the equipment that how to perform noise reduction on the collected signal data to obtain pure component vibration signals is also a difficult problem.Especially in the background of working conditions and its complex mining and metallurgical equipment.Secondly,when it comes to maintenance and maintenance decisions for parts and components,due to deviations in the judgment of the parts life cycle and its inaccurate description of the degradation process,the resulting maintenance decisions are not scientific enough.In view of this,this article is based on the "method for the prediction of service life of typical parts and components of mining and metallurgical equipment and maintenance decision-making method",and uses the typical parts of the intermediate shaft of the mining and metallurgy equipment and the barrel extrusion tube as research carriers to carry out related research.(1)First of all,a study was carried out on how to obtain a characteristic index that can better characterize the degradation process of the shaft of mining and metallurgical equipment.The time-domain,frequency-domain,and time-frequency domain analysis of the vibration signal data of the collected components were separately performed.The data processing software was used to graphically process the degradation process described by them,and then the rotation axis degradation process theory was used.The intuitive judgment method makes subjective feature index selection;Then,the principal component analysis method is used to reduce the dimensions of many feature indicators,analyze the contribution of their information content,find the best feature index combination,and finally fuse them.The effects of the degraded process described after the fusion verify that the selected indicators can better reflect the declining performance of the shaft.(2)With the help of the nonlinear time series projection method,the sampled signal containing background noise can be reconstructed,and the feature signal and the noise signal are projected onto different spaces to improve the noise reduction performance of the dual-tree complex wavelet to make it more effective.Then,the conditions of working conditions on the service shaft are classified by fuzzy clustering method,and the degradation process function of the shaft is fitted by using artificial neural network.Finally,input the noise-reduced sample data to predict,re-analyze the prediction results with the clustering center,analyze the current state of the shaft according to the calculated Euclidean distance,and finally determine the remaining service life of the shaft.(3)The maintenance threshold and maintenance interval have been optimized to establish a real-time fault-tolerant optimization model.Using the Gamma distribution to describe the entire deteriorating process of the barrel extrusion cylinder,it is assumed that the time-consuming maintenance of the barrel is subject to an exponential distribution and the proposed maintenance interval is determined.Based on the maintenance cost,downtime loss,and equipment utilization,a decision model function that optimizes preventive maintenance thresholds and maintenance intervals and targets the lowest maintenance cost ratio is established.