Performance monitoring and fault-tolerant control of complex systems with variable operating conditions

Ensuring the reliable operation of engineering systems has long been a subject of great practical and academic interest. This interest is clearly demonstrated by the preponderance of literature in the area of Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC), spanning the past three decades. However, increasingly stringent performance and safety requirements have led to engineering systems with progressively increasing complexity. This complexity has rendered many traditional FDD and FTC methods exceedingly cumbersome, often to the point of infeasibility.

This thesis aims to enable FDD and FTC for complex engineering systems of interacting dynamic subsystems. For such systems, generic FDD/FTC methods have remained elusive. Effects caused by nonlinearities, interactions between subsystems and varying usage patterns complicate FDD and FTC. The goal of this thesis is to develop methods for FDD and FTC that will allow decoupling of anomalies occurred inside the monitored system from those occurred in the systems affecting the monitored system, as well as enabling performance recovery of the monitored system.

In pursuit of these goals, FDD and FTC methods are explored that can account for operating regime-dependent effects in monitoring, diagnosis, prognosis and performance recovery for two classes of machines: those that operate in modes that can change only at distinct times (which often occur in manufacturing opera- tions such as drilling, milling, turning) and for those that operate in regimes that are continuously varying (such as automotive systems or electric motors).

For machines that operate in modes that can change only at distinct times, a degradation model is postulated which describes how the system degrades over time for each operating regime. Using the framework of Hidden Markov Models (HMMs), modeling and identification tools are developed that enable identification a HMM of degradation for each machine operation. In the sequel, monitoring and prognosis methods that naturally follow from the framework of HMMs are also presented. The modeling and monitoring methodology is then applied to a real-world semiconductor manufacturing process using data provided by a major manufacturer.

For machines that operate in regimes that are continuously varying, a behavioral model is postulated that describes the input-output dynamics of the nor- mal system in different operating regimes. Monitoring methods are presented that have the capability to account for operating regime-dependent modeling accuracies and isolate faults that have not been anticipated and for which no fault models are available. By conducting fault detection in a regime-dependent fashion, changes in modeling errors that are due to operating regime changes can be successfully distinguished from changes that are due to truly faulty operation caused by changes in the system dynamics. Enabled by this, unanticipated faults can be isolated through proliferation of the fault detection through the various subsystems of the anoma- lous system. The FDD methodology is applied to detect and diagnose faults for a multiple-input multiple-output Exhaust Gas Recirculation system in a diesel engine.

Finally, methods to facilitate the recovery of normal system behavior are detailed. Using the same local model structure that was pursued for behavioral models, it is envisioned that the nominal controller will be reconfigured to attempt to recover nominal behavior as much as possible. To enable this reconfiguration, methods for automated design of closed-loop controllers for the local modeling structure are presented. Using a model-predictive approach with rigorous stability considerations, it is shown that the controllers can track a reference trajectory. Such a trajectory could be generated by any model that satisfies the control objectives, for normal or faulty systems. The controllers are then demonstrated on a benchmark nonlinear system that is nonlinear in the control.