Research And Application Of Fault Prediction Analysis Method For Intelligent Production Line

Nowadays,with the development of science and technology,more and more enterprises have set up intelligent production lines to cope with the increasingly fierce competition environment.At the same time,more and more equipment with complex mechanical structure are introduced into the production line,which puts forward new requirements for equipment maintenance.The traditional methods of "regular maintenance" and "post-maintenance" have been unable to meet the requirements of modern production.The failure occurred in the production process not only affects the production efficiency,but also poses a threat to the personal safety of workers.If we can predict the potential failure points in the production line,we can work out the appropriate maintenance plan in advance,which can not only reduce the impact on the production work,but also can purposefully repair the equipment.At the same time,it can also improve the effective service life of the equipment and save extra costs for the enterprise.Therefore,how to effectively predict the failure of the production line and provide certain basis for the maintenance of equipment is an urgent problem to be solved.In view of the above problems,this paper analyzes the mechanism of equipment failure by combining with the miniature experimental production line platform,and develops the research work of equipment state prediction and production line fault diagnosis.Then the work flow of the corresponding algorithm is given,which has certain practical value for improving the accuracy of fault prediction on the production line.The specific research contents of this paper are as follows:1.The failure mechanism analysis of experimental production line equipment.Firstly,the main equipment structure is introduced,and then the fault analysis tree model is established on the basis of fault mechanism analysis of the working purpose and working stress of each type of equipment.Finally,the core monitoring index types of the main equipment are given.2.Establishment of equipment state prediction model.Aiming at the problem of "error accumulation" in the prediction process of Long Short-Term Memory neural network,a Dual-LSTM hybrid model method for equipment state prediction is proposed.Firstly,the method of data set construction is given,and then the prediction workflow of prediction model and auxiliary model is introduced.Finally,the feasibility of the proposed method is verified on the experimental data set.3.Establishment of production line fault diagnosis model.Firstly,the processing method of support vector machine for linear indivisible sample set and multiclassification is introduced,and then the parameters to be set are optimized by particle swarm optimization algorithm.Finally,this paper proposes the fault diagnosis steps based on PSO-SVM algorithm and verifies the effectiveness of the method.4.Design of fault prediction and analysis system for production line.In order to monitor the running state of the production line more intuitively,a production line fault prediction and analysis system is developed based on Qt platform.The system mainly includes data management module,status monitoring module,status prediction module,fault diagnosis module and maintenance advice module,which can more easily monitor the status of production line equipment and carry out fault warning,making the system more practical.