Data mining techniques for predicting values of a faulty sensor at a refinery

Refineries rely heavily on the sensor data, decisions making in critical situation when a sensor failure happens is therefore essential. This paper proposes a method of predicting sensor values based on its historical data captured as time series. Main forecasting techniques such as linear regressio...

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Bibliographic Details
Main Authors: Saybani, M. R., Teh Ying Wah, Lahsasna, A., Amini, A., Aghabozorgi, S. R.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Data models
Forecasting
Mathematical model
Oil Refinery
Predictive models
Sensor
Smoothing methods
Time Series
Time series analysis
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Summary:Refineries rely heavily on the sensor data, decisions making in critical situation when a sensor failure happens is therefore essential. This paper proposes a method of predicting sensor values based on its historical data captured as time series. Main forecasting techniques such as linear regression, moving average, autoregressive integrated moving average; and exponential smoothing were used to predict the value of failed sensors. A comparison of the models based on their mean squared error is presented in order to simplify the selection of forecasting models. The proposed model assists engineers and experts at a refinery to make critical decision at critical moments.