Kurzfassung	6
	Abstract	8
	Acknowledgements	9
	Contents	10
	List of Acronyms and Abbreviations	14
	List of Symbols	16
	1 Introduction	19
	1.1 Motivation	24
	1.2 Focus of the Work	25
	1.3 Structure and Format	26
	2 Scientific State of the Art	28
	2.1 Information Fusion	28
	2.1.1 Uncertainty	32
	2.1.2 Conflict	34
	2.2 Information Models	35
	2.2.1 Probability Theory Fusion Approaches	36
	2.2.2 Dempster-Shafer Theory of Evidence Fusion Approaches	39
	2.2.3 Fuzzy Set Theory Fusion Approaches	43
	2.2.4 Possibility Theory Fusion Approaches	46
	2.2.5 Hybrid Information Fusion Approaches	47
	2.2.6 Further Information Models	48
	2.3 Human Group Decision-Making	49
	2.4 Scientific Gap	50
	2.5 Chapter Summary	52
	3 Preliminaries	53
	3.1 Modified-Fuzzy-Pattern-Classifier Membership Function Training	53
	3.2 An Interconnection Between Dempster-Shafer, Fuzzy Set, and Possibility Theory	56
	3.3 Two-Layer Conflict Solving	59
	3.3.1 Conflict-Modified-DST	60
	3.3.2 Group-Conflict-Redistribution	61
	3.4 Fuzzy Aggregation	63
	3.4.1 Ordered Weighted Averaging	64
	3.4.2 Implicative Importance Weighted Ordered Weighted Averaging	66
	3.5 Truncated Triangular Probability-Possibility Transform	67
	3.6 Monitoring of Sensor Reliability	68
	3.7 Chapter Summary	71
	4 Multilayer Attribute-based Conflict-reducing Observation	72
	4.1 The MACRO Architecture	73
	4.2 Information Source Signal Conditioning	76
	4.3 System State Representation	77
	4.4 Fuzzy Basic Belief Assignment	80
	4.5 Attribute Layer Fusion	82
	4.5.1 Analysis of Two-Layer Conflict Solving	84
	4.5.2 Balanced Two-Layer Conflict Solving	95
	4.5.3 Fuzzified Balanced Two-Layer Conflict Solving	103
	4.5.4 MACRO Attribute Layer Fusion	105
	4.5.5 Conflicting Coefficient Behaviour	107
	4.5.6 Conflict as a Measure of Importance	109
	4.5.7 MACRO Attribute Structure	110
	4.6 System Layer Fusion	110
	4.6.1 Degree of Optimism	111
	4.6.2 Attribute Importance	113
	4.7 Sensor Defect Detection	114
	4.7.1 Sensor Observation Determination	115
	4.7.2 Measurement Scale Fuzzification	115
	4.7.3 Majority Consistency Measure Adaptation	117
	4.7.4 Groupwise Sensor Reliability Determination	118
	4.7.5 Sensor Defect Decision Rule	119
	4.8 Implementation Aspects	119
	4.8.1 Matrix Notation	120
	4.8.2 Matrix Decomposition	122
	4.8.3 Computational Complexity	125
	4.9 Chapter Summary	126
	5 Evaluation	128
	5.1 Implementations	129
	5.2 Human Activity Recognition	129
	5.2.1 Experiment Setup	131
	5.2.2 Experiment Results	135
	5.2.3 Discussion of the Results	141
	5.3 Condition Monitoring Under Laboratory Conditions	142
	5.3.1 Experiment Setup	145
	5.3.2 PU static Data Set Results	148
	5.3.3 PU manip Data Set Results	153
	5.3.4 Discussion of the Results	160
	5.4 Information Fusion Robustness Towards Noise	161
	5.5 Sensor Defect Detection	164
	5.5.1 PU static Data Set Results	165
	5.5.2 PU manip Data Set Results	166
	5.6 Chapter Summary	167
	6 Summary	168
	6.1 Conclusion	170
	6.2 Future Work	172
	6.2.1 Information Fusion System Design	173
	6.2.2 Information Fusion System Composition and Adaptation	174
	Appendix A Foundations of Probability Theory	177
	Appendix B Foundations of Dempster-Shafer Theoryof Evidence	181
	Appendix CFoundations of Fuzzy Set Theory	185
	Appendix DProofs	187
	D.1 Proofs of Section 4.4	187
	D.2 Proofs of Section 4.5.1	188
	D.3 Proofs of Section 4.5.2	191
	D.4 Proofs of Section 4.5.3	191
	D.5 Proofs of Section 4.8	196
	Appendix E Compliance of the Fuzzy Basic Belief Assignment Approach with Dempster-Shafer Theory of Evidence	200
	Appendix F Features Involved in Condition Monitoring Evaluation	204
	F.1 Static Printing Unit Demonstrator Operation (PUstatic)	204
	F.2 Manipulated Printing Unit Demonstrator Operation(PUmanip)	206
	F.3 Noisy Manipulated Printing Unit Demonstrator Operation (PUmanip)	208
	Appendix G Determination of OWA Weights with Desired Andness	213
	Appendix HBrief Historical Background	215
	H.1 Information Fusion	215
	H.2 Fuzzy Set Theory	216
	Bibliography	218
	List of Figures	241
	List of Tables	245
	Theses	248