Fault detection and diagnosis of technical systems

Aim

To give both a theoretical and practical basis for how to design systems that automatically detect and isolate faulty components in technical processes.

After completing the course, the student shall be able to:

• Based on a mathematical model of a technical process, apply model-based methods to analyze diagnostic performance and to detect and isolate faults.
• Based on historical data from a technical process apply data-driven methods to detect and classify faults.

Content

1. Introduction to fault diagnosis, design of diagnostic systems, examples of industrial applications.2. Mathematical modeling for fault detection and fault isolation using models, consistency relations, analytical redundancy.3. Structural methods for fault diagnosis, bipartite graphs, modeling for structural analysis, matching, analysis of structural diagnosis properties, algorithms for finding overdetermined equation sets for residual generation.4. Linear and nonlinear residual generation, observers and Kalman filters for diagnosis.5. Statistical methods for fault detection.6. Fault isolation, decisions with structured hypothesis tests, minimal hitting set.7. Data-driven fault diagnosis, anomaly detection, classification.8. Hybrid fault diagnosis combining model-based and data-driven diagnosis methods.