Theory And Applications Of The Soft Sensing Technology Based On Machine Learning Algorithms

Relations exist universally in nature and are expressed in certain forms, which is the basic philosophy foundation of this research subject. In mathematicians'view, this kind of relations is a kind of mappings, or mapping functions. In the information age, these mapping relations are contained within many thousands of data. The soft sensing technology (SST), being researched in this dissertation, aims at finding various mapping functions hidden in data. Today, in the age that industrialization is driven by the information technology (IT), the instrumentation and measurement technology is an important part of the information science and technology. With people requiring higher quality and enhancing security awareness and environment protection, various testing, measuring and analyzing instruments are demanded increasingly. Furthermore, as an emerging technology, the SST is widely applied in the industrial measurement and control field. Therefore, research on the SST is very important to the development of instrumentation and measurement technology.Usually, a practical industrial process has a complex nonlinearity and is polluted by lots of noises, which limits the applications of traditional SSTs based on the mechanism analysis, multivariate linear regression (MLR) and artificial neural network (ANN). To overcome the limitations of the traditional SSTs, our research puts emphasis on studying the nonlinear soft sensor modeling methods that have good generalization capability and strong robustness. The research work is based on the machine learning (ML) theory. Under the precondition of guaranteeing the model's generalization capability (GC) and robustness, some modified algorithms are studied and developed to improve the modeling efficiency. Main research work and productions are listed as follows:(1) Modify the distance definition of traditional k-nearest neighbors (kNN) algorithm by replacing the standard Euclidian distance with attribute-weighted distance. And develop a dataset editing algorithm based on the modified kNN algorithm for filtering inconsistent samples. Propose a fast kNN algorithm for medium- or large-scale datasets. Its running efficiency is only affected by the neighbor number k and the dimension of dataset n. Usually, it runs several up to twenty times faster than the traditional algorithm. As for developing the locally-approximated learning, there is universal sense to study the algorithms that can fast search for the kNN sub-dataset.(2) Research the soft sensor modeling methods based on multiple neural networks (MNN), which aims at improving the model's GC and robustness in the industrial environment. Propose an MNN model that uses clustering sub-datasets as validation datasets (not training datasets), based on which a two-layer MNN model is built. Two comparison experiments are performed over the pulp Kappa dataset for four different models, including single ANN, ensemble MNN, modular MNN and two-layer MNN. Experiment results show that the two-layer MNN model outperforms other three models on the robustness and GC.(3) Apply the soft margin SVM regression algorithm to the soft sensor modeling. Give two versions of theε-SVMR algorithm and its two implementing methods, i.e., the universal quadratic programming solver and sequential minimal optimization (SMO) algorithm. Over the pulp Kappa dataset, two experiments are performed to study how free parameters to impact the performance of theε-SVMR algorithm and to compare the modeling efficiency of two implementing methods. The main conclusion is that the SVMR method, especially the SMO algorithm, is fit for soft sensor modeling of practical industrial processes.(4) Study two prediction modeling methods using time series (TS) sampled from a process, i.e., the temporal difference trained neural network (TDNN) and SVMR algorithm. Two methods train prediction models over the sample-expanding dataset and feature-expanding dataset, respectively. Two experiments are carried out on the TSs of the kraft pulping process. The experiment results show the multi-step expanding prediction exceeds the single-step prediction, especially for the small-sized TS set. And the feature-expanding SVMR method exceeds the sample-expanding TDNN method, especially for the single-sequence TS set.(5) Do theoretical research on the process neural network (PNN) and reveal the relationship between the process neuron and the traditional neuron. Point out that the process neuron can be approximated infinitely using the traditional neuron, present two approximating theorems and their detailed proof, and give two related corollaries. Experiments are performed to validate the PNN method over a set of function-generated sine wave coded signals. The experiments prove that the PNN can suppress white noises and enhance the robustness of the model. However, applying the PNN need choose a certain function orthogonal basis.(6) Define the inner product and norm of signals (functions), and propose a novel process learning algorithm, i.e., the process SVM (PSVM). Experiments are carried out to compare the PSVM with the PNN over the same set of signals as above. Avoid choosing a function orthogonal basis, which makes the PSVM more convenient to be applied than the PNN. When the noise amplitude is small, the PSVM model outperforms the PNN model; when the noise amplitude is rather large, the PSVM model performs a little worse than the PNN model. But its performance can be improved by representing signal samples with finite basis functions.Creative achievements obtained in our research include: (1) Propose a dataset editing method based on the modified kNN algorithm for filtering inconsistent samples in a dataset. (2) Present a fast kNN algorithm, which has a universal sense for studying local learning (lazy learning) methods. (3) To improve the generalization ability of a model, present an MNN model that uses clustering sub-datasets as validation datasets. (4) Propose two approximating theorems to the process neuron, which reveal the relationship between the process neuron and the traditional neuron. (5) Present a novel process learning algorithm, namely, the PSVM. In conclusion, this research work focuses on studying theories and applications of the SST based on the machine learning. After doing in-depth research work, we obtain some useful achievements. The soft sensing approaches proposed in the dissertation not only enrich the soft sensing theory, but also promote the industrial utilization of the SST. The last two chapters pay attention to theoretical research. The theoretical fruits will push the development of the soft sensing theory as well as the ML theory. Limit to the author's knowledge and experience, mistakes and faults are hard to avoid in the dissertation. Please point them out and give some comments or suggestions."In the past, we mined gold with electromechanical machines; In the Knowledge Economy age, we are to mine gold with learning machines. Our gold mines are the databases stored in factories and enterprises."...