Modeling the stochastic mechanism of sensor using a hybrid method based on seasonal autoregressive integrated moving average time series and generalized estimating equations

Sensor, which is one of the main components of control system, plays its vital role in measuring state and output system variables and highlights the importance of having desired statistical information about sensor output signals because it can be monitored, stored, or used as the primary input signal in other devices. However, these signals display noises (i.e. system noise and measurement noise) and even if the effects of system noises are faded away or removed from measured data, there is still some stochastic noise remained in the measurements. Even though SARIMA has been effective in modeling the stochastic noise in the sensor, the present study has found out the necessity of designing a novel approach including a combination of seasonal autoregressive integrated moving average (SARIMA) and polynomial generalized estimating equations (PGEE), to evaluate the stochastic behavior of sensors. Finally, the study tried to employ the proposed approach in real load-cell sensor data to examine its effectiveness. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study explores the necessity of evaluating the stochastic behavior of sensors by combining SARIMA and PGEE methods and attempts to apply this approach to real load-cell sensor data.