Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
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.