Fault diagnosis and maintenance optimization for interconnected systems: With applications to railway and climate control systems

For many systems, like medical devices, nuclear reactors, and transportation systems, an adequate maintenance optimization approach is essential to ensure high levels of reliability and safety while keeping operational costs low. A promising approach towards this goal is condition-based maintenance, which plans maintenance only when the system health indicates a need for it. To infer the system health, monitoring devices are installed to collect health-related data. The path from the monitoring data to a maintenance schedule then involves the following steps:

1. fault diagnosis, i.e. detecting abnormal system behavior and identifying its cause;
2. failure prognosis, i.e. predicting future system health;
3. maintenance optimization, i.e. determining the required type of maintenance as well as the optimal time to perform the maintenance task.

Although various methods have been published for all three tasks, discrepancies still exist between the assumptions made in the literature and the conditions encountered in practice. These discrepancies include, e.g., unrealistic assumptions regarding the absence of component interdependencies and regarding the (number of) available monitoring signals. This thesis contributes to resolving these discrepancies by proposing methods for fault diagnosis, failure prognosis, and maintenance optimization, particularly focusing on narrowing the gap between theory and practice. When treating the individual tasks, the dependencies between fault diagnosis, failure prognosis, and maintenance optimization are explicitly taken into account.