Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Thiyagarajan, Karthick; Kodagoda, Sarath; Nguyen, Linh

doi:10.1109/iciea.2017.8283153

“…Since the seasonal ARIMA model in that study is combined with Hyndman and Khandakar algorithm, the optimization parameters of the model are chosen automatically. This makes the forecasting model a hybrid type because of the reason that the ARIMA model optimization parameters can control the forecasting model to be either the ARIMA model or Autoregressive (AR) model or Moving Average (MA) model [16]. Similar studies were conducted for the sewer pipe surface temperature sensor, where the seasonal ARIMA model was used for forecasting a day period to develop an early sensor failure prediction model [15].…”

Section: Brief Review On Related Workmentioning

confidence: 99%

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Thiyagarajan¹,

Kodagoda²,

Ulapane³

2020

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Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

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“…This predictive analytics based corrosion estimation needs longterm sensor inputs. However, sensors can produce random anomaly or a continuous stream of anomalies in sewer environmental conditions [20], [21]. Hence, it is important to have a diagnostic tool to automatically detect anomalies in sensors such as sewer air temperature sensor, which provides crucial data inputs to the models predicting corrosion.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Thiyagarajan¹,

Kodagoda²,

Ulapane³

et al. 2020

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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.

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“…Moreover, the forecasted sensor data can be an ideal alternative to real sensor measurements during the disrupted monitoring period. Another benefit that can be derived from the forecast data is the detection of anomalies and early sensor failure prediction by comparing the forecast data with the streaming sensor measurements [15], [16]. Hence, the forecasted sensor data is important and it can be fed as surface temperature data input to the predictive analytics model for estimating corrosion.…”

Section: Introductionmentioning

confidence: 99%

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Thiyagarajan¹,

Kodagoda²,

Ulapane³

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

show abstract

Predictive analytics for detecting sensor failure using autoregressive integrated moving average model

Cited by 38 publications

References 25 publications

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

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