Research And Faulty Diagnosis Of Large Compressor System Based On Intelligent Control

The development of online monitoring and fault diagnosis system is an integrated technology involved in different subject and domain. Some technologies cooperated with diagnosis are included, such as sensor, data Collection, signal analysis, data processing, database, and net transmission. A perfect fault diagnosis system should be joined with the technology of fault theory, monitoring and trend anticipation and so on. With these efforts, we can reduce the accidents, improve the work efficiency and contribute more to the nation.Fault diagnosis system used in modern industry can solve the problem of machinery fault identification. Safeguard system can solve the problem of fatal accident prevention. DCS can solve the problem of technical parameter adjust. But there is no such flexible fault preventing and protecting system in modern industry that machinery isn't in the best condition in operation. Based on the main 3 kinds of industry-aided systems, the opinion of fault prevention and diagnosis of rotary machinery is presented.Based On sufficient study of working condition, mechanical property and product requirement of the research object, the theory design of fault prevention and diagnosis of rotary machinery is presented in this paper. The whole system includes three parts: the theory used in the paper, data processing method and control system, the examples of fault prevention and diagnosis. The system terminates fault inducement and makes machines run continuously as long as possible in healthy state.Centrifugal compressor is one of the most important mechanisms in chemical plants, and it's necessary for us to monitor its working conditions. By now, most of the chemical plants have equipped with online monitoring systems for centrifugal compressors, but few of those systems have fault diagnosis functions. Considerable attention has been devoted to the study of intelligent fault diagnosis for large rotating mechanisms in recent years, and all kinds of advanced intelligent diagnosis theories have been applied to this research. Especially the fashionable method of artificial neural networks, which has powerful abilities of function approximation and pattern recognition, has been widely used in fields of nonstationary time series forecasting and fault diagnosis.Forecasting parameters that reflecting the equipment state, and diagnosis for some probable faults are two most important parts in system of state monitoring and fault diagnosis. In this paper, application of RBF(Radial Basis Function) neural network and Adaline(Adaptive Linear Element) neural network for nonstationary time series forecasting is discussed, and they have been successfully applied to the vibration forecasting of centrifugal compressor. On the other hand, fault diagnosis for centrifugal compressor based on wavelet transform and artificial neural network is also studied in this paper. An experiment system of state monitoring and fault diagnosis for centrifugal compressor is developed by our project group, and the mainly job for the author is to compile software modules, including thermal performance monitoring module and fault diagnosis module. A universal and modularized program is also developed based on theoretical research and experimental results, which is suitable for digital signal processing and wavelet transform and artificial neural networks analysis.