Sensor failure detection and faulty data accommodation approach for instrumented wastewater infrastructures

Thiyagarajan, K; Kodagoda, S; Van Nguyen, L; Ranasinghe, R

Sensor failure detection and faulty data accommodation approach for instrumented wastewater infrastructures

Thiyagarajan, K

Kodagoda, S

Van Nguyen, L

Ranasinghe, R

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Publication Type:: Journal Article
Citation:: IEEE Access, 2018, 6 pp. 56562 - 56574
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Van Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IEEE Access, 2018, 6 pp. 56562 - 56574	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132652
dc.description.abstract	© 2013 IEEE. In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2018.2872506	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Sensor failure detection and faulty data accommodation approach for instrumented wastewater infrastructures	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.for	0907 Environmental Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2013 IEEE. In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.

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http://hdl.handle.net/10453/132652