A Possibilistic Framework for Sensor Fusion with Monitoring of Sensor Reliability

Sensor and Information fusion is recently a major topic, not only in traffic management, military, avionics, robotics, image processing, and e. g. medical applications, but becomes more and more important in machine diagnosis and conditioning for complex production machines and process engineering. Several approaches for multi-sensor systems exist in the literature, cf. (Hall & Llinas, 2001); (Bosse et al., 2007). The diagnosis and flaw detection in machines and process plants is a complex task, which is dependent on many effects. In the past decades, autonomous and automatic systems have become ubiquitous in our daily life. As famous examples, one may cite advanced driver-assistance systems in vehicles, quality monitoring in production processes, or semi-autonomous unmanned aircraft systems in military forces. All these systems have the common feature that they (partially) capture the state of the environment and generate fused information based on the data gained. More technically, the analogous environment is captured applying several sources (sensors, experts, etc.). After obtaining these analogous signals and experts’ information, they are preprocessed and transformed into the digital domain, so that they can be processed on computers. In many cases the information captured from the environment may be imprecise, incomplete or inconsistent. Furthermore, signal sources may be not reliable. Therefore, it is necessary to extend known fusion concepts insofar that they are able to handle and to measure imprecision and reliability. In this chapter we will highlight, to our best knowledge, a new human-centric based fusion framework. Additionally, an application of the fusion method is shown for printing machines in the area of quality inspection and machine conditioning. The goal is the early recognition of errors in machines in order to avoid flaws by combining measuring data from several sensors with expert knowledge for the improvement of quality by

[1]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[3]  H. Larsen,et al.  Importance weighted OWA aggregation of multicriteria queries , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[4]  Catherine K. Murphy.  Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[5]  Karl R. Popper.  The Logic of Scientific Discovery. , 1977 .

[6]  S. Ahlawat,et al.  Order effects and memory for evidence in individual versus group decision making in auditing , 1999 .

[7]  Ronald R. Yager,et al.  Classic Works of the Dempster-Shafer Theory of Belief Functions , 2010, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[8]  Bilal M. Ayyub.  Uncertainty Modeling and Analysis , .

[9]  Lotfi A. Zadeh,et al.  A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..

[10]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[11]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[12]  D. Dubois,et al.  Operations on fuzzy numbers , 1978 .

[13]  Ronald R. Yager,et al.  Fusion of fuzzy information with considerations for compatibility, partial aggregation, and reinforcement , 1996, Int. J. Approx. Reason..

[14]  W. Dyck,et al.  A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion , 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

[15]  Lotfi A. Zadeh,et al.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008, IEEE Computational Intelligence Magazine.

[16]  Hedvig Kjellström,et al.  An information fusion demonstrator for tactical intelligence processing in network-based defense , 2007, Inf. Fusion.

[17]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[18]  F. Ramsey.  The Foundations of Mathematics and Other Logical Essays , 2001 .

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[21]  J. Keynes.  A Treatise on Probability. , 1923 .

[22]  François Delmotte,et al.  Detection of defective sources in the setting of possibility theory , 2007, Fuzzy Sets Syst..

[23]  A. Kolmogoroff.  Grundbegriffe der Wahrscheinlichkeitsrechnung , 1933 .

[24]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[25]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[26]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[27]  D. Dubois,et al.  Possibility theory and data fusion in poorly informed environments , 1994 .

[28]  Didier Dubois,et al.  Possibility Theory and its Applications: a Retrospective and Prospective view , 2006, Decision Theory and Multi-Agent Planning.

[29]  Galina L. Rogova,et al.  Reliability In Information Fusion : Literature Survey , 2004 .

[30]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[31]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[32]  Simona Salicone,et al.  Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence , 2006 .

[33]  Volker Lohweg,et al.  A NOVEL DATA FUSION APPROACH USING TWO-LAYER CONFLICT SOLVING , 2008 .

[34]  Henrik Legind Larsen,et al.  Generalized conjunction/disjunction , 2007, Int. J. Approx. Reason..

[35]  Jean Roy,et al.  Concepts, Models, and Tools for Information Fusion , 2007 .

[36]  Ronald R. Yager,et al.  A framework for multi-source data fusion , 2004, Inf. Sci..

[37]  Henrik Legind Larsen,et al.  Efficient importance weighted aggregation between min and max. , 2002 .

[38]  Rudolf Carnap.  Probability as a Guide in Life , 1947 .

[39]  D. Dubois,et al.  Possibility theory in information fusion , 2000, Proceedings of the Third International Conference on Information Fusion.

[40]  George J. Klir,et al.  Uncertainty Modeling and Analysis in Engineering and the Sciences (Hardcover) , 2006 .

[41]  R. Yager.  Aggregation operators and fuzzy systems modeling , 1994 .

[42]  Florentin Smarandache,et al.  Advances and Applications of DSmT for Information Fusion (Collected Works) , 2004 .

[43]  Helly.  Grundbegriffe der Wahrscheinlichkeitsrechnung , 1936 .

[44]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[45]  Ian Hacking,et al.  The Emergence of Probability: Duality , 2006 .

[46]  M. Black.  Vagueness. An Exercise in Logical Analysis , 1937, Philosophy of Science.

[47]  E. Salas,et al.  Taking stock of naturalistic decision making , 2001 .

[48]  Didier Dubois,et al.  An information-based discussion of vagueness , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[49]  Uwe Mönks,et al.  Fuzzy-Pattern-Classifier Based Sensor Fusion for Machine Conditioning , 2010 .

[50]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[51]  Uwe Mönks,et al.  Sensor fusion by two-layer conflict solving , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[52]  Fabio Campos,et al.  Decision Making in Uncertain Situations: An Extension to the Mathematical Theory of Evidence , 2006 .