Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems (Hardcover)

1. Introduction 1.1 Process automation and supervision 1.2 Product life cycle and fault management (asset management) 1.3 Contents I SUPERVISION, FAULT DETECTION AND DIAGNOSIS 2. Supervision, fault detection and fault diagnosis methods 2.1 Basic tasks of supervision 2.2 Terminology 2.2.1 Faults, failures, malfunctions 2.2.2 Reliability, availability, safety 2.2.3 Fault tolerance and redundancy 2.3 Knowledge based fault detection and diagnosis 2.4 Signal based fault detection methods 2.4.1 Limit checking 2.4.2 Trend checking 2.4.3 Change detection 2.4.4 Adaptive thresholds 2.4.5 Plausibility checks 2.4.6 Signal analysis methods 2.5 Processmodel based fault detection methods 2.5.1 Process models and fault modeling 2.5.2 Fault detection with parameter estimation 2.5.3 Fault detection with state observers and state estimation 2.5.4 Fault detection with parity equations 2.5.5 Direct reconstruction of not measurable variables 2.6 Fault diagnosis methods 2.6.1 Classification methods 2.6.2 Inference methods 2.7 Fault detection in closed loops 2.8 Data flow structure for supervision II DRIVES AND ACTUATORS 3. Fault diagnosis of electrical drives 3.1 Direct current motor (DC) 3.1.1 Structure and models of the DC motor 3.1.2 Fault detection with parity equations 3.1.3 Fault detection with parameter estimation 3.1.4 Experimental results for fault detection 3.1.5 Experimental results for fault diagnosis with SELECT 3.1.6 Conclusions 3.2 Alternating current motor (AC) 3.2.1 Structure and models of induction motors 3.2.2 Signal based fault detection of power electronics 3.2.3 Model based fault detection of an AC motor 3.2.4 Concusions 4. Fault diagnosis of electrical actuators 4.1 Electromagnetic actuator 4.1.1 Position control 4.1.2 Fault detection with parameter estimation 4.2 Electromagnetic automotive throttle valve actuator 4.2.1 Structure and models of the actuator 4.2.2 Input test cycle for quality control 4.2.3 Fault detection with parameter estimation 4.2.4 Fault detection with parity equations 4.2.5 Fault diagnosis 4.2.6 Fault diagnosis equipment 4.2.7 Conclusions 4.3 Brushless DC motor actuator and aircraft cabin pressure valve 4.3.1 Structure and models 4.3.2 Fault detection with parameter estimation 4.3.3 Fault detection with parity equations 4.3.4 Conclusions 5. Fault diagnosis of fluidic actuators 5.1 Hydraulic servo axis 5.1.1 Hydraulic servo axis structure 5.1.2 Faults of hydraulic servo axes 5.1.3 Models of the spool valve and cylinder 5.1.4 Fault detection and diagnosis of the valve and cylinder 5.1.5 Fault diagnosis 5.1.6 Conclusions 5.2 Pneumatic actuators 5.2.1 Pneumatic flow valve structure and modeling 5.2.2 Fault detection and diagnosis with local linear models 5.2.3 Experimental results 5.2.4 Conclusions III MACHINES AND PLANTS 6. Fault diagnosis of pumps 6.1 Centrifugal pumps 6.1.1 Status of pump supervision and fault detection 6.1.2 Models of a centrifugal pump and pipe system 6.1.3 Fault detection with parameter estimation 6.1.4 Fault detection with nonlinear parity equations and parameter estimation 6.1.5 Conclusions 6.2 Reciprocating pumps 6.2.1 Structure of a diaphragm pump 6.2.2 Models of a diaphragm pump 6.2.3 Fault detection and diagnosis of the hydraulic part 6.2.4 Fault detection of the pump drive 6.2.5 Conclusions 7. Leak diagnosis of pipelines 7.1. Status of pipeline supervision 7.2 Mathematical models of pipelines 7.3 Model based leak detection 7.3.1 Leak detection with state observers 7.3.2 Leak detection with mass balance and correlation analysis for liquid pipelines 7.3.3 Leak detection for gas pipelines 7.4 Experimental results 7.4.1 Gasoline pipeline 7.4.2 Gas pipeline 7.4.3 Conclusions 8. Fault diagnosis of