An FPGA-based spectral anomaly detection system

Anomaly detection based on spectral features is applicable to a diverse range of problems including prognostic and health management, vibration analysis, astronomy, biomedicai engineering and computational finance. The input data could be regularly sampled, as in the case of a standard analogue to digital converter sampling a bandlimited signal at above the Nyquist rate, or irregularly sampled, as in the case of stock quotes or astronomical data. In this paper, we present new online algorithms for the computation of power spectra for regularly or irregularly sampled data, and performing anomaly detection on time series data. Both algorithms allow hardware implementations with O(l) time complexity, this being the minimum for any system that considers all the samples. We combine the two algorithms to form a power Spectrum-based Anomaly Detector (SAD). We also describe an implementation of SAD which has minimal hardware requirements, and achieves one to two orders of magnitude improvement in speed, latency, power and energy over a traditional processor-based design.