Research On Data-driven Fault Detection And Diagnosis Techniques And Their Applications

With continuously improved requirements for the reliability and security of complex industrial processes and the accuracy of disease diagnosis, fault detection and diagnosis techniques have become intensively researched topics and found wide applications in the process monitoring, biomedical diagnosis and many other practical problems. On the one hand, the integration degree of process systems is improved and the interactions among the components and parts are increased, putting forward higher requirements for the industrial process monitoring. On the other hand, due to the complexity of modern medical diseases,doctors may make the deviated or even wrong analyses if they diagnose only on the basis of their personal experience and knowledge. Therefore, it is necessary to develop objective and efficient disease diagnosis methods for obtaining accurate results.With the rapid development of the Internet and information management systems, the amount of collected and stored process data has grown exponentially. Reasonable detection and diagnosis models should be constructed by extracting important information from a large scale of data. In addition, various factors such as time, cost and privacy may restrict the collection of disease data, and these data may contain a lot of redundant feature parameters.So we need to develop high performance diagnosis systems based on high-dimensional small sampled data. In this context, with the rapid progress of computer network, data mining and pattern recognition technologies, the data-driven methods of statistic machine learning based process monitoring and intelligent computing based medical diagnosis have emerged and received intensive attentions.At present, the data-driven fault detection and diagnosis methods have made fruitful progress in the process monitoring field and most of them construct models based on some assumptions such as single operating mode, linear process and stationary condition. However,due to the adjustment of market strategy and changes of product specifications and manufacturing conditions, the process data can not satisfy these assumptions. As a result, the performances of these monitoring methods are not good enough. This thesis provides a series of process monitoring methods through detailed analysis and systematical research for specific monitoring challenges in industrial processes in order to gain satisfactory monitoring results. Meanwhile, based on the high-dimensional and small amount of disease data, we propose an intelligent diagnosis strategy by focusing on how to select important features related to the disease. The main research work of this thesis is summarized as follows.1. For multimode and nonlinear process monitoring, a probabilistic kernel principal component analysis mixture model(PKPCAM) is developed from the viewpoint of probability. Firstly, the probabilistic kernel principal component analysis mixture model is constructed in the high-dimensional feature space, and the multimode data are characterized as multiple local components. Then, according to the Bayesian inference, the posterior probability of local components is integrated with Mahalanobis distance, and a global statistic index is obtained for measuring the deviation of the test samples from normal operation.Unlike the traditional kernel principal component method and the k-means basedsub-principal component analysis method, the PKPCAM can adaptively describe the process multimodality and nonlinearity and achieve the superior detection and diagnosis performance.2. For dynamic and nonlinear process monitoring, a kernel independent component analysis(KICA) based pattern matching approach is proposed. Based on the analysis of dynamic process data, we conduct automatic matching on the normal benchmark and monitored set through the sliding windows strategy and pattern matching method. A new dissimilarity detection index which integrates the angle measurement with distance measurement is designed in the high-dimensional independent subspace. Furthermore, the contribution of each process variable is extracted by utilizing the mutual information between the variables. The presented method is applied to the waste water treatment process and exhibits good fault detection and diagnosis effects.3. The complex process data often contain normal operations mixed with various types of faults. A local discriminant analysis(LDA) based hyperplane distance neighbor clustering(HDNC) method is proposed to overcome the defect that most traditional monitoring methods require fault-free normal data to build an operation model. Various unlabeled fault samples are separated from the normal ones using the HDNC method. The LDA algorithm is also taken into account, so that the proposed method not only extracts the inherent discriminant information of faults, but also describes the tight data clustering. Simulations results from the Tennessee Eastman process and waste water treatment process indicate that the proposed method has good detection and classification capability for complex chemical processes.4. For the multiphase batch process, after the batchwise unfolding of process data, a global-local discriminant analysis(GLDA) based Gaussian process regression(GPR)approach is developed. The hidden Markov model can identify different phases of the batch process due to its stochastic and inferential characteristics. The GLDA algorithm is applied to extract the process variables correlated with the quality output, eliminate the redundant variables and decrease the model complexity before regression modeling. Besides, the multiple local GPR models for corresponding to different phases are built. The local GPR model with the maximal matching degree is chosen for online quality prediction. The effectiveness of the proposed method is demonstrated by employing it to the multiple-phase penicillin fermentation process.5. For the small amount of high-dimensional disease data, a genetic algorithm(GA)based feature selection method is proposed. The optimal feature subset is selected by the optimization and searching of GA, requiring no prior knowledge about the cardinality of feature subset. The selected feature subset can not only describe the discriminant information of various data classes, but also consider the redundant degree among the features, and is independent of the specific classifiers. Compared to the common feature selection methods and all feature set, the proposed method has the superior classification performance through three different classifiers on the standard lung cancer dataset.