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.

传感器是控制系统的主要组成部分之一，在测量状态和输出系统变量方面发挥着至关重要的作用，并强调了获得有关传感器输出信号的所需统计信息的重要性，因为它可以被监控、存储或用作主要的其他设备的输入信号。然而，这些信号显示出噪声（即系统噪声和测量噪声），即使系统噪声的影响从测量数据中消失或消除，测量中仍然存在一些随机噪声。尽管 SARIMA 在模拟传感器中的随机噪声方面很有效，但本研究发现有必要设计一种新方法，包括结合季节性自回归积分移动平均 (SARIMA) 和多项式广义估计方程 (PGEE)，评估传感器的随机行为。最后，该研究试图在真实的称重传感器数据中采用所提出的方法来检查其有效性。