Recent developments in measurement and processing techniques have been used to monitor the stability of oscillations directly. Combined with fast, real-time data processing, this technology allows real-time monitoring of the margin of safety maintained against potential instability. This technology has been used to detect and warn of the system approaching instability. The use of real-time continuous dynamics monitoring often indicates dynamic behaviour that was anticipated by the modelbased studies. In such cases it can be difficult to track down the sources of problems using conventional tools. This paper details the possibility of diagnosing the causes of problems related to oscillatory stability using measurement-based techniques. A dynamic model based on a real system is used to simulate periods of instability so that the methodology can be applied to the system to determine significant variables that contribute to mode dynamics. The aim of this paper is to develop a statistical model to encompass the linear and non-linear relationships in the system, which can then be used to identify causes of unstable system dynamics. To this end the discrete wavelet transform is used in conjunction with generalized linear models to fit the model data and to predict the system response with minimum deviance. This robust model could then be used in real time with real system variables to determine the correct course of action in rectifying a range of dynamics problems.