Channel-wise reconstruction-based anomaly detection framework for multi-channel sensor data

Mingu Kwak, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Anomaly detection is the task of learning patterns of normal data and identifying data with other characteristics. As various types of sensors are attached to vehicle, healthcare equipment, production facilities, etc., detecting anomalies in multi-channel sensor data has become very important. In sensor data, abnormal signals occur temporally during certain intervals of a few channels. It is very important to capture the characteristics of individual channel and cross-channel relationship in order to detect abnormal signals that occur locally for a short time interval. We propose a channel-wise reconstruction-based anomaly detection framework which consists of two parts: channel-wise reconstruction part with convolutional autoencoder (CAE) and anomaly scoring part with machine learning algorithms, isolation forest (iForest) and local outlier factor (LOF). CAE learns the features of normal signal data and measures channel-wise reconstruction error. We applied the symmetric skip-connections technique to build a CAE model for higher reconstruction performance. Given the channel-wise reconstruction error as an input, iForest and LOF summarize it to anomaly score. We present our results on data collected from real sensors attached to vehicle and show that the proposed framework outperforms traditional reconstruction-based anomaly detection methods and one-class classification methods.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
EditorsYaxin Bi, Rahul Bhatia, Supriya Kapoor
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030295127
Publication statusPublished - 2020
EventIntelligent Systems Conference, IntelliSys 2019 - London, United Kingdom
Duration: 2019 Sept 52019 Sept 6

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceIntelligent Systems Conference, IntelliSys 2019
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
This research was supported by the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency in the Culture Technology Research & Development Program 2019.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • Anomaly detection
  • Convolutional autoencoder
  • Multi-channel sensor data

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


Dive into the research topics of 'Channel-wise reconstruction-based anomaly detection framework for multi-channel sensor data'. Together they form a unique fingerprint.

Cite this