Anomaly detection in time series data using a combination of wavelets, neural networks and Hilbert transform
Stratis A. Kanarachos, Jino Mathew, Alexander Chroneos, Michael E. Fitzpatrick · 2015
In this paper, a new signal processing algorithm for detecting anomalies in time series data is proposed. Real time detection of anomalies is crucial in structural health monitoring applications as it can be used for an early detection of structural damage as well as for discovery of abnormal operating conditions that can shorten a structure's life. A new algorithm - a combination of wavelets, neural networks and Hilbert transform - is presented and discussed in this study. The algorithm has been evaluated for a number of benchmark tests, commonly used in the literature, and has been found to perform robustly.