Predictive Analytics for Detecting Sensor Failure Using Autoregressive Integrated Moving Average Model

Publication Type:
Conference Proceeding
12th IEEE Conference on Industrial Electronics and Applications, 2017
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
P1456 (1).pdfAccepted Manuscript731.19 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: