Automatic Monitoring, Fault Diagnostics, and Maintenance based on Meta-heuristic Optimization

This thesis deals with a meta-heuristic approach to enhance automatic fault monitoring in man-made systems. Fault monitoring indicates the set of logical steps appropriate to the detection, identification, and prevention of anomalies in an entire system or structure. In particular, in literature, such steps are defined as: fault detection, fault isolation, in-time prognosis and predictive maintenance. On the other hand, the attribute meta-heuristic focuses on computational methods to solve optimization problems by trying to improve a candidate solution iteratively with regard to a given index of quality. Therefore, in the present thesis, for each of the above phase of fault monitoring (i.e. fault detection, isolation, and so on), a meta-heuristic approach is proposed and tested experimentally in an actual industrial environment. The methods proposed in the thesis were validated on specific problems, but their applicability is wide and general intrinsically.