Anomaly detection from online monitoring of system operations using recurrent neural network

Taiki Kubota, Watalu Yamamoto

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

In production systems, the operation data is multivariate time series data including internal state of the system, control variables, control parameters and the like. As monitoring centre collects data intensively, monitoring time differs for each system. The predetermined frequency of data recording per day may not be protected. In this study, we decided to use a RNN which can learn data with missing values. The neural network learns diagnosis of system abnormality from the operation data of the system and the data of the maintenance record. Then we examine the usefulness of prediction of abnormal occurrence of learned neural network.

Original languageEnglish
Pages (from-to)83-89
Number of pages7
JournalProcedia Manufacturing
Volume30
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event14th Global Congress on Manufacturing and Management, GCMM 2018 - Brisbane, Australia
Duration: 2018 Dec 52018 Dec 7

Keywords

  • Interpolation
  • Missing Values
  • Multivariate time series data
  • Recurrent neural network
  • Time series classification
  • Trouble prediction

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Anomaly detection from online monitoring of system operations using recurrent neural network'. Together they form a unique fingerprint.

Cite this