Unsupervised pattern recognition methods for exploratory analysis of industrial process data

The rapid growth of data storage capacities of process automation systems provides new possibilities to analyze behavior of industrial processes. As existence of large volumes of measurement data is a rather new issue in the process industry, long tradition of using data analysis techniques in that field does not yet exist. In this thesis, unsupervised pattern recognition methods are shown to represent one potential and computationally efficient approach in exploratory analysis of such data. This thesis consists of an introduction and six publications. The introduction contains a survey on process monitoring and data analysis methods, exposing the research which has been carried out in the fields so far. The introduction also points out the tasks in the process management framework where the methods considered in this thesis – self-organizing maps and cluster analysis – can be benefited. The main contribution of this thesis consists of two parts. The first one is the use of the existing and development of novel SOM-based methods for process monitoring and exploratory data analysis purposes. The second contribution is a concept where cluster analysis is used to extract and identify operational states of a process from measured data. In both cases the methods have been applied in exploratory analysis of real data from processes in the wood processing industry.

[1]  Anthony O'Hagan,et al.  Kendall's Advanced Theory of Statistics, volume 2B: Bayesian Inference, second edition , 2004 .

[2]  Jarkko Venna,et al.  Coloring that Reveals Cluster Structures in Multivariate Data , 2000 .

[3]  John S. Oakland,et al.  Statistical Process Control , 2018 .

[4]  Olli Simula,et al.  Process State Monitoring Using Self-Organizing Maps , 1992 .

[5]  Timo Sorsa,et al.  Neural networks in process fault diagnosis , 1991, IEEE Trans. Syst. Man Cybern..

[6]  Ossi Taipale,et al.  Optimization and simulation of quality properties in paper machine with neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[7]  Pekka Teppola,et al.  Adaptive Fuzzy C-Means clustering in process monitoring , 1999 .

[8]  Ignacio Díaz Blanco,et al.  Complex Process Visualization through Continuous Feature Maps Using Radial Basis Functions , 2001, ICANN.

[9]  Tariq Samad,et al.  Self–organization with partial data , 1992 .

[10]  C. Apte,et al.  Data mining: an industrial research perspective , 1997 .

[11]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[12]  A. J. Hoffman,et al.  The application of neural networks to vibrational diagnostics for multiple fault conditions , 2002, Comput. Stand. Interfaces.

[13]  Marie Cottrell,et al.  Analyzing and representing multidimensional quantitative and qualitative data , 1999 .

[14]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[15]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[16]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[17]  Juha Vesanto,et al.  Data exploration process based on the self-organizing map , 2002 .

[18]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[19]  Robert P. W. Duin,et al.  Novelty Detection Using Self-Organizing Maps , 1997, ICONIP.

[20]  Jose A. Romagnoli,et al.  Data Processing and Reconciliation for Chemical Process Operations , 1999 .

[21]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[22]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[23]  Hiroyuki Mori,et al.  An artificial neural-net based technique for power system dynamic stability with the Kohonen model , 1991 .

[24]  Eamonn J. Keogh,et al.  An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback , 1998, KDD.

[25]  Geoff Barton,et al.  Process control: Designing processes and control systems for dynamic performance , 1996 .

[26]  Peter C. Cheeseman,et al.  Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.

[27]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[28]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[29]  H. C. Card,et al.  Linguistic interpretation of self-organizing maps , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[30]  T. Harris A Kohonen SOM based, machine health monitoring system which enables diagnosis of faults not seen in the training set , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[31]  Olli Simula,et al.  SOM Based Analysis of Pulping Process Data , 1999, IWANN.

[32]  W. T. Williams,et al.  Dissimilarity Analysis: a new Technique of Hierarchical Sub-division , 1964, Nature.

[33]  M. Vermasvuori,et al.  Industrial applications of the intelligent fault diagnosis system , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[34]  William L. Luyben,et al.  Process Modeling, Simulation and Control for Chemical Engineers , 1973 .

[35]  D. Niebur,et al.  Power system static security assessment using the Kohonen neural network classifier , 1991 .

[36]  Samuel Kaski,et al.  Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997 , 1998 .

[37]  Klaus Schulten,et al.  Self-organizing maps: ordering, convergence properties and energy functions , 1992, Biological Cybernetics.

[38]  Juha Tuominen,et al.  PROCESS ERROR DETECTION USING SELF-ORGANIZING FEATURE MAPS , 1991 .

[39]  Alvy Ray Smith,et al.  Color gamut transform pairs , 1978, SIGGRAPH.

[40]  Kevin Warwick,et al.  Multivariable cluster analysis for high-speed industrial machinery , 1995 .

[41]  Pasi Koikkalainen,et al.  SOM Based Visualization in Data Analysis , 1997, ICANN.

[42]  Peter Smith,et al.  'NEURAL-MAINE': intelligent on-line multiple sensor diagnostics for steam turbines in power generation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[43]  Alberto Muñoz,et al.  Self-organizing maps for outlier detection , 1998, Neurocomputing.

[44]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[45]  Jaideep Srivastava,et al.  Event detection from time series data , 1999, KDD '99.

[46]  Theodosios Pavlidis,et al.  Waveform Segmentation Through Functional Approximation , 1973, IEEE Transactions on Computers.

[47]  C. McGreavy,et al.  Automatic Classification for Mining Process Operational Data , 1998 .

[48]  Hiroshi Furukawa,et al.  A systematic method for rational definition of plant diagnostic symptoms by self-organizing neural networks , 1996, Neurocomputing.

[49]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[50]  Olli Simula,et al.  An approach to automated interpretation of SOM , 2001, WSOM.

[51]  Johan Himberg,et al.  A SOM based cluster visualization and its application for false coloring , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[52]  Timo Kostiainen,et al.  Generative probability density model in the self-organizing map , 2001 .

[53]  David Mautner Himmelblau,et al.  Fault detection and diagnosis in chemical and petrochemical processes , 1978 .

[54]  R. J. Patton,et al.  Soft Computing Approaches to Fault Diagnosis for Dynamic Systems: A Survey , 2000 .

[55]  Olli Simula,et al.  Representation and Identification of Fault Conditions of an Anaesthesia System by Means of the Self-Organizing Map , 1994 .

[56]  Fouad Badran,et al.  Hierarchical clustering of self-organizing maps for cloud classification , 2000, Neurocomputing.

[57]  Esa Alhoniemi,et al.  SOM Toolbox for Matlab 5 , 2000 .

[58]  Juha Vesanto,et al.  Hunting for Correlations in Data Using the Self-Organizing Map , 1999 .

[59]  A. W. Kemp,et al.  Kendall's Advanced Theory of Statistics. , 1994 .

[60]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[61]  Michèle Basseville,et al.  Detecting changes in signals and systems - A survey , 1988, Autom..

[62]  J. Rantanen,et al.  Visualization of fluid-bed granulation with self-organizing maps. , 2001, Journal of pharmaceutical and biomedical analysis.

[63]  J. R. Leigh Applied Digital Control: Theory, Design, and Implementation , 1984 .

[64]  Olli Simula,et al.  Monitoring industrial processes using the self-organizing map , 1999, SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269).

[65]  Jouko Lampinen,et al.  On the generative probability density model in the self-organizing map , 2002, Neurocomputing.

[66]  Patrick Rousset,et al.  Analyzing and Representing Multidimentional Quantitative an Qualitative Data: Demographic Study of the Rhone Valley. The Domestic Consumption of the Canadian Families , 1999 .

[67]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[68]  Olli Simula,et al.  Process Monitoring and Visualization Using Self-Organizing Maps , 1995 .

[69]  C. McGreavy,et al.  Data Mining and Knowledge Discovery for Process Monitoring and Control , 1999 .

[70]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[71]  Esa Alhoniemi,et al.  Probabilistic measures for responses of Self-Organizing Map units , 1999 .

[72]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[73]  Erkki Oja,et al.  PicSOM - content-based image retrieval with self-organizing maps , 2000, Pattern Recognit. Lett..

[74]  Esa Alhoniemi,et al.  Simplified time series representations for efficient analysis of industrial process data , 2003, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[75]  O. Simula,et al.  The Self-organizing map as a tool in knowledge engineering , 2000 .

[76]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[77]  Petri Vuorimaa,et al.  A Defect Detection Scheme for Web Surface Inspection , 2000, Int. J. Pattern Recognit. Artif. Intell..

[78]  C. M. Crowe,et al.  Data reconciliation — Progress and challenges , 1996 .

[79]  Olli Simula,et al.  Neural analysis of mobile radio access network , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[80]  D. Hawkins POINT ESTIMATION OF THE PARAMETERS OF PIECEWISE REGRESSION MODELS. , 1976 .

[81]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[82]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[83]  Chris Aldrich,et al.  The interrelationship between surface froth characteristics and industrial flotation performance , 1996 .

[84]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[85]  Sampsa Laine,et al.  On-line determination of the concentrator feed type at Outokumpu Hitura mine , 2000 .

[86]  Theodora Kourti,et al.  Statistical Process Control of Multivariate Processes , 1994 .

[87]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[88]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[89]  Samuel Kaski,et al.  Comparing Self-Organizing Maps , 1996, ICANN.

[90]  Jure Zupan,et al.  Kohonen and counterpropagation artificial neural networks in analytical chemistry , 1997 .

[91]  Thomas J. McAvoy,et al.  Fault Detection and Diagnosis in Industrial Systems , 2002 .

[92]  Alfred Ultsch,et al.  Self Organized Feature Maps for Monitoring and Knowledge Aquisition of a Chemical Process , 1993 .

[93]  Johan Himberg,et al.  Enhancing SOM-based data visualization by linking different data projections , 1998 .

[94]  Chonghun Han,et al.  Intelligent systems in process engineering : A review , 1996 .