A temporal forecasting driven approach using Facebook's Prophet method for anomaly detection in sewer air temperature sensor system

Karthick Thiyagarajan, Sarath Kodagoda, Nalika Ulapane

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

18 Citations (Scopus)

Abstract

![CDATA[Smart sensor systems play a decisive role in the condition assessment of concrete sewer pipes going through microbial corrosion. Few Australian water utilities adopt a predictive analytic model for estimating the corrosion. They require sensor inputs like sewer air temperature data for corrosion prediction. A sensor system was developed to monitor the daily variation of sewer air temperature inside the harsh sewer environmental conditions. However, a diagnostic tool to evaluate the streaming sensor data is vital for reliable monitoring. In this context, this paper proposes a temporal forecasting driven approach for anomaly detection in sewer air temperature sensor system. Several temporal forecasting models were comprehensively evaluated and adopted Facebook's Prophet method based forecasting to develop an anomaly detection approach. The proposed approach was evaluated with sewer air temperature sensor data and the results indicate a reasonable anomaly detection performance.]]
Original languageEnglish
Title of host publicationProceedings of the 15th IEEE Conference on Industrial Electronics and Applications (ICIEA 2020), 9 - 13 November 2020, Virtual
PublisherIEEE
Pages25-30
Number of pages6
ISBN (Print)9781728151694
DOIs
Publication statusPublished - 2020
EventIEEE Conference on Industrial Electronics and Applications -
Duration: 1 Jan 2021 → …

Conference

ConferenceIEEE Conference on Industrial Electronics and Applications
Period1/01/21 → …

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