Fault detection in a network of similar machines using clustering approach

Fault detection, which involves the estimation of the condition, health or degradation of an equipment or a process and a decision logic to determine whether an event that can be considered as a fault has occurred, is an integral component in prognostics and health management because it is an essential indicator when to perform fault diagnosis and isolation, and it also precedes any performance prediction methodology. The implementation of data-driven fault detection has generally been reliant on unit-specific models which can be less effective with insufficient training data or when used in applications with non-stationary working conditions. The aforementioned scenarios can be alleviated by leveraging on data from similar units experiencing comparable operating regimes. This dissertation investigates the formulation, development and implementation of a cluster-based fault detection to a fleet of similar machines. A two-step approach is introduced: fleet clustering and local cluster fault detection. Fleet clustering verifies, discovers and identifies the group structure of the network of machines. Afterwhich, the health of each unit in the cluster is assessed using peer-to-peer comparison. The approach developed in this dissertation is validated with two case studies: a fleet of industrial welding robots from an automotive manufacturing facility and a group of wind turbines from several wind farms.