Study On On-Line Fault Diagnosis System And Key Technologies In Process Industry Production Based On Pattern Recognition

As the development of process industry towards large-scale,complexity and intelligence,doing and designing a good rapid on-line intelligent fault diagnosis system is becoming an important objection in the process industry and intelligent science filed.And this on-line system should be able to on-linely detect the faults, on-linely diagnose faults,on-linely recognise the variables which may incur the faults happening,and onlinely update the learning ability and knowledge of this system in the processes of the production.Assisted by this on-line system,the engineers can easily do the on-line fault detection and on-line fault diagnosis so as to locate the reasons of the faults,i.e.making the production of process industry much safer.And then improve the efficiency of the enterprise.With the development of the process industry CIPS(Computer Integrated Process system),a great amount of process data can be sampled and collected to store in database.How to fully mine this deep-level information to improve the performance of the process monitoring has been gradually becoming one of the focuses in the field of process control and intelligenct science.This thesis mainly focuses on studying and designing an on-line fault diagnosis system method for process industry based on patten recognition tools of SVDD (Support Vector Data Description)and RF(Random Forestes).The main contributions of this thesis are as follows:(1)To address the problem of kernel parameterσselection and regulation of the decision boundary in SVDD algorithm,this thesis proposes a new kernel parameter optimization method based on the spheral distribution of samples in kernel space and regulation of the decision boundary method based on KPCA(Kernel Principal Component Analysis).Firstly,this optimization method utilizes non-Gaussian to measure how the kernel samples approximate to a spheral area so as to select a better kernel parameter.If the kernel parameter has been selected,the distribution of kernel samples is still possible with uneven distribution.This thesis applies KPCA in depth to regulate the decision boundary in order to arrive at high classification performance.(2)To address the problem of SVDD processing larget samples dataset with huge time complexity,this thesis proposes a novel RGInc-SVDD(Random Greed Incremental SVDD)algorithm.Firstly,using the SL(Sampling Lemma)to divide the training samples dataset into several small samples subsets;sencondly,create an Inc-SVDD_i model with one of samples subsets;then,apply rule of interactive random greed to grow the Inc-SVDD_i until the SVDD being created with whole training samples information.The RGBInc-SVDD algorithm makes the time complexity significantly decrease from 0(n³)to 0(floor(n/k)³),where n,k respectively denote the number of training samples and the number of random greed in each interactive step.(3)To address the problem of decision boundary of SVDD with more lax,this thesis proposes a new data description algoritm named KMVEE(Kernel Minimum Volume Enclosing Ellipsoid).As like the SVDD algorithm,KMVEE is also looking for a Minimum Volume Enclosing Hyper-Ellipsoid to enclose all the traning samples as more as possible.Under the same kernel parameter,the KMVEE shows the better performance than the SVDD,because the decision boundary of KMVEE is tighter than one of the SVDD.(4)To address the problem of known fault diagnosis or multi-class classification(some samples falling inside of decision boundary,i.e.,X_inside),this thesis proposes a new M-SVDD algorithm named RTIM-SVDD(Rejected Transductive Inference Multi-SVDD).Being different from the distance M-SVDD, the RTIM-SVDD applies M+1 hyper-sphere as decision boundary to form M-SVDD classification.To solve a bottleneck problem of labeling fuzzy samples, we adopt a new transductive inference to label these fuzzy samples.Transductive inference mainly makes use of measurement confidence to deal with labeling fuzzy samples,and shows better performance than tranditional M-SVDD.(5)To address the problem of unknown fault diagnosis or clustering(some samples falling outside of decision boundary,i.e.,X_outside),this thesis proposes a new improved SVC(Support Vector Clustering)algorithm.This proposed method employs Steepest Descent Gradient Method to hunt for local optimization points and then use TLCG(Three Line Completed Graph)rule to assign labels of these points so as to cluster all samples.The time complexit of SVC in assigning labels of cluster is significantly decrease from 0(n²m)to 0(n_op²m),where m denotes the number of selected points on the line between two traning samples and n_opdenotes the local optimizatial solution.(6)To address the problem of unknown fault locations,this thesis proposes a new method based on RTIM-SVDD and RF(Random Forests).The fault location based on RTIM-SVDD utilizes the performance index P_ROCto locate the fault reasons.The fault location based on RF mainly improves a serial of process:the Bagging split of decision trees and variables importance computation so as to locate the fault reasons.The above proposed methods are validated based on the following dataset:UCI data set,TEP(Tennessee Eastman Process)dataset and QAMADICS(Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) dataset.The results demonstrate the feasibility of these proposed methods.Finally,there are concluded with a summary and some further research areas in this thesis.