6
Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis
2. Model-based fault diagnosis a. Neural network b. Fuzzy logic analysis c. Genetic algorithm d. Artificial intelligence e. Finite-element (FE) magnetic circuit equivalents f. Linear-circuit-theory-based mathematical models 3. Machine-theory-based fault analysis a. Winding function approach (WFA) b. Modified winding function approach (MWFA) c. Magnetic equivalent circuit (MEC) 4. Simulations-based fault analysis a. Finite-element analysis (FEA) b. Time-step coupled finite element state space analysis (TSCFE-SS)
The different types of fault diagnosis methods have been simultaneously applied to fine-tune the detection in industry. The fault diagnosis of electrical motors is expected to provide warning of imminent failures, diagnosing scheduling information for future preventive maintenance. The implementation of fault diagnosis has been done with the following routine:
1. Fault detection a. Time-domain-based detection (mostly for power system fault diagnosis) b. Frequency domain-based detection (mostly for signal-based machine fault diagnosis) c. Accumulated data-based detection (mostly for model-based fault diagnosis) 2. Fault decision making a. Decide fault existence b. Decide fault severity 3. Feedback to motor controller or human interface a. Limit motor operation based on fault severity b. Schedule maintenance
Figure 1.4 shows the increased convergence between the energy system and modern network system in modern industry. The electrical motors in a car, ship, aircraft, building, road, or in a power system can be assumed to be