Research On Fault Diagnosis In The Tomato Sauce Production Process Control

Relying on the superior natural conditions, the planting of processing tomato and the production of tomato sauce have become one of the important pillar industries of Xinjiang. In recent years, the production of tomato sauce in Xinjiang increases year by year, and its sales has reach nearly 1/3 share of the international market. The production of Tomato sauce has the distinct seasonal characteristics, once production malfunction caused the halt, which namely can cause a lot of waste of the raw material of processing tomato and then directly impacts the production efficiency of the factory. Therefore the integrity rate of the production equipment of tomato sauce, the fault diagnosis of production process and troubleshooting become a problem of tomato sauce production equipment design and manufacture, which is also concerned by the manufacturers of tomato sauce.As the development of automation technology in the industrial control field, intelligent algorithm is more and more applied to fault diagnosis field, especially the expert system has been applied early. But the expert system exist shortcomings of knowledge-acquiring difficulty, which makes its application scope limited. This paper puts forward one algorithm which fuses neural network and expert system, then designs and realizes the fault diagnosis system of neural-network-and-expert system, and carries on the research of fault diagnosis methods and technology to two paragraphs of sterilization and evaporation process in tomato-sauce production process.Based on the simulation analysis which uses MATLAB for neural network algorithm, the paper applied neural-network-and-expert system to diagnosis the possible kinds of fault in sterilization and evaporation process of tomato sauce, and then applies in WinCC monitoring system and achieves good results. Besides, in the control system of sterilization, the paper uses the method that programming in the organization which has the interrupt function, and then realizes the diagnosis to the process control system.