- Title
- Predictive analytics for detecting sensor failure using autoregressive integrated moving average model
- Creator
- Thiyagarajan, Karthick; Kodagoda, Sarath; Van Nguyen, Linh
- Date
- 2017
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183942
- Identifier
- vital:16398
- Identifier
-
https://doi.org/10.1109/ICIEA.2017.8283153
- Identifier
- ISBN:978-1-5090-6161-7
- Abstract
- Sensors play a vital role in monitoring the important parameters of critical infrastructure. Failure of such sensors causes destabilization to the entire system. In this regard, this paper proposes a predictive analytics solution for detecting the failure of a sensor that measures surface temperature from an urban sewer. The proposed approach incorporates a forecasting technique based on the past time series of sparse data using an autoregressive integrated moving average (ARIMA) model. Based on the 95% forecast interval and continuity of faulty data, a criterion was set to detect anomalies and to issue a warning for sensor failure. The forecasted and faulty data were assumed Gaussian distributed. By using the probability density of the distribution, the mean and variance were computed for faulty data to examine the abnormality in the variance value of each day to detect the sensor failure. The experimental results on the sewer temperature data are appealing.
- Publisher
- IEEE
- Relation
- 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA); Siem Reap, Cambodia; 18-20 June 2017 p. 1926-1931
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Subject
- Anomalies detection; ARIMA model; Data models; Forecasting; Mathematical model; Monitoring; Predictive models; Sensor failure detection; Sewers; Surface temperature; Temperature measurement; Temperature sensors; Time series; Time series analysis
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