Design And Implementation Of Furnace Temperature Monitoring System For Continuous Annealing Line Based On Pca

The process monitoring and fault diagnosis is one of the most important issues in the process industry. The method can not only timely detect the fault, interference and the abnormal process condition by mornitoring the state of production process, but also locate and diagnose the factos which cause the faults. The safety of production process will be assured and the quality of the product will be improved by these characteristics. With the development of CIPS (Compute Integrated Process System), a great amount of process data can be sampled and collected. How to use the deep-level information to improve the performance of the process monitoring is becoming one of the focuses in the field of process control.PCA (Principal Component Analysis), which is introduced into the field of process industry, is a signal decomposing technique based on statistical information. This method can utilize the statistical characteristics of the variable more efficiently. The intrinsic characteristics of the process can be described through the monitoring variables by means of the statistical property.Based on the continuous annealing line, the method of PCA is adopted to moniter the furnace temprature. Firstly, aiming at the characteristics of mass data, complicated variables and high real time, the database of the monitoring system of the continuous annealing line is designed and implemented, and further more, the database is optimized and designed the backup strategy in order to ensure fast and stable running of the system. According to the requirements of the operation and software design, this thesis is divided into eight modules to design and develop the cleint of the monitoring system. Based on the PCA algorithm, the thesis has realized the function of monitoring and fault diagnosis.Through practical operation, the PCA algorithm in the monitoring and fault diagnosis of the continuous annealing line is proved to be suitable and reliable and it is becoming an important tool for online monitoring, fault diagnosis and quality improvement. It is also provide the basis for application of PCA to large scale industrial processes.