Auxiliary power unit (APU) is a gas turbine engine on aircraft that provides energy for functions other than propulsion. Its starter is a crucial component that outputs assistant power to support the APU starting process. Starter performance degradation significantly impairs the whole APU life and raises risks for the aircraft flight. The aim of this thesis is to propose a framework for enabling the online detection and prediction of starter degradation. An online classifier based on moving autocorrelation is designed to detect the initial phase of degradation for the starter’s failure. Also, a particle filtering based approach with an associated system state model is proposed to achieve the fault diagnostics and failure prognostics. The results demonstrate that a condition based maintenance program for the APU starter can be implemented to avoid unnecessary economic losses and to enhance aircraft operating safety.