Application Research On Intelligent Multi-faults Recognition Methods In Process Monitoring

As the development of science and technology, industry process is becoming more and more complicated. In order to ensure process safety and reliability, decrease the production costs and improve products' quality, related researchers have developed many kinds of design program, control strategy and optimization algorithm. Process monitoring is a systematic engineering, it is integrated with automation control, artificial intelligent, computer science, pattern recognition and system engineering knowledge. This paper is focused on industry process monitoring, especially about multi-class faults recognition. Main research work is presented as follows:Principle component analysis (PCA) is usually combines with Hotelling T2 and SPE statistical methods to solve fault detection and classification problem with linear and little sample label. Fisher data was used in the second section. Train a PCA model with one class of the sample data; calculate the T2 and SPE control limits of the train samples, all the test samples' T2 and SPE value would be compared with control limits to decide whether it is abnormal data or not. Meanwhile, the other two class data was projected to the principle space. It can be obviously discovered from the 2-dimension distribution plot that different class is scattered in different space.In the third part of this paper, PCA_SVM is presented as a multi-label recognition method. Traditional PCA is used as a dimension decrease tool, then all fault data is projected into the normal PCA space, detect the new coming data with Hotelling T2. All the detected data would be transport to the SVM classifier to judge which class the sample belong to. From the experiment based on the TE data, it can be concluded that PCA_SVM method is simple, fast computation and higher classification accuracy.Manifold learning method is introduced in the last section. Local tangent space alignment algorithm (LTSA) which is one of the manifold learning method, imported as a dimension reduction method. A combined support vector machine (CSVM) classification is created in this part. During the multi-labels SVM classification process, each lvsl classifier will choose an appropriate kernel function Instead of just one kernel function for all the classifiers. After experiment test, the LTSA_CSVM shows some advantages in the multi-label classification.