Anomaly detection in time series data using a combination of wavelets, neural networks and Hilbert transform

Research output: Contribution to conferencePaper

11 Citations (Scopus)
276 Downloads (Pure)

Abstract

Real time detection of anomalies is crucial in structural health monitoring applications as it is used for early detection of structural damage and to identify abnormal operating conditions that can shorten the life of operating structures. A new signal processing algorithm for detecting anomalies in time series data is proposed in this study. The algorithm is expressed as a combination of wavelet analysis, neural networks and Hilbert transform in a sequential manner. The algorithm has been evaluated for a number of benchmark tests, commonly used in the literature, and has been found to perform robustly.
Original languageEnglish
DOIs
Publication statusPublished - 2015
Event6th IEEE International Conference on Information, Intelligence, Systems and Applications - Corfu, Greece
Duration: 6 Jul 20158 Jul 2015

Conference

Conference6th IEEE International Conference on Information, Intelligence, Systems and Applications
Abbreviated titleIISA2015
CountryGreece
CityCorfu
Period6/07/158/07/15

Bibliographical note

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • anomaly detection
  • wavelets
  • neural networks
  • Hilbert

Fingerprint Dive into the research topics of 'Anomaly detection in time series data using a combination of wavelets, neural networks and Hilbert transform'. Together they form a unique fingerprint.

Cite this