Key Technologies Research Of Fault Diagnosis System For Large Aluminum Profile Extrusion Production Line Based On Hybrid System Model

Large aluminum extrusion production line (LAEPL) is a complex industrial process control system, which is a combination of a variety of mechanical parts, hydraulic union and electrical unit including the frequency furnace, extrusion machine, the main auxiliary oil pump station, hydraulic control system, electrical control system of main auxiliary equipment,the monitoring device, etc. The rate of failures in the LAEPL is high for it is very difficult to diagnose these faults in LAEPL from people’s practical experience, thus the research in intelligent fault diagnosis technique is of vital importance and very significant.This paper is a tentative research on key technologies of fault diagnosis in LAEPL by using a series of technologies. These technologies include integrated fault modeling technology, hydraulic fault diagnosis technology, sensor fault diagnosis technology, nonlinear system state estimation theory, intelligent fault diagnosis theory and other advanced theories and algorithms. On the basis of theoretical research, a fault diagnosis system in LAEPL is initiated and developed in this paper.This paper is expected to put forward a fault diagnosis model integrated with TPN-HBG by conducting some researches on the integrated fault modeling method, and based on hybrid system is supposed to be integrated in this model. This model can overall reflect continuous variables and discrete events, and would satisfy the requirement of diagnostic model in the complex process control system. Based on the integrated fault diagnosis model, two fault diagnosis methods in the system layer and the device layer are designed. To be more specific, a LAEPL fault diagnosis method in the system layer was designed. A multi-loop complex hydraulic system will be put forward in the device layer. This system is a kind of HBG-TCG based fault diagnosis method for LAEM (Large Aluminum profile Extrusion Machine) with multi hydraulic source for detecting and diagnosing the key equipment failure-LAEM.In order to diagnose electro-hydraulic servo system of LAEM, a brand-new model based on hybrid systems and multiple linear regression models is proposed in this dissertation by using multi sensor information fusion, a model based on hybrid systems and multiple linear regression models. The condition and the fault of electro hydraulic servo system are abstracted by using hybrid system theory, and the electro hydraulic servo system model of hybrid system is further established. Also the parameters of the system are identified by the use of multiple linear regression algorithms. Experiments show that two fault diagnosis methods are effective in fault diagnosis of hydraulic parameters monitoring and fault forecasting.For the diversity in the quantity and the type of sensors on LAEPL, this paper will propose good evidence to show the uncertainty correction algorithm on the basis of the smallest discount, and this algorithm can distinguish the sensor faults, equipment failure and environmental disturbance. In this paper, a residual generator is constructed to realize the sensor fault detection and isolation through combining the theory of linear system observer. Also by using the geometric theory of the invariant subspace, the fault feature decoupling will be achieved through feature space partition. The sensor fault detection and isolation system will be further realized by using space projection operation. By doing these, not only the single fault can be detected and isolated by the method, but also multi sensor fault can be detected and isolated effectively.In the field of state estimation research in aluminum extrusion, the extrusion processes is represented as a linear system through the analysis of the extrusion thermodynamic theory, and a nonlinear disturbance model is proposed by using ARMR model. By combining an adaptive-network-based fuzzy inference system with "one-to-one mapping", a compensator for unmodeled dynamics is constructed. Based on the research on state estimation in aluminum extrusion and the analysis of extrusion process, the relationship between the control parameters and the temperature rise is derived A discrete state estimation model based on Hidden Markov is designed; the most likely state can be determined by using the Bays formula. This method can be used to correctly estimate the extrusion process as well as the liquid-solid phase mutation probability prediction.On the basis of the research in the state estimation and fault diagnosis method, the fault diagnosis system for LAEPL will be developed in this paper, and the LAEPL running status monitoring and fault diagnosis system will also be designed for the actual need. This system has been successfully applied in55MN aluminum extrusion practice monitoring of a aluminum CO.,LTD, and it can also be used in the real time state monitoring and fault diagnosis.