In order to achieve the desired operating configuration in any process plant, the system status needs to be accurately measured. Also, for a system to operate within the desired limit, it is essential to know the reliability of the plant measurements. Therefore, the demand for robust and resilient performance has led to the use of online-monitoring techniques to monitor the process parameters and signal validation. On-line monitoring and signal validation techniques are the two important terminologies in process and equipment monitoring. These techniques are automated methods of monitoring instrument performance while the plant is operating [1, 2]. To implementing these techniques, several empirical models are used [1-4]. One of these models is nonparametric regression model, otherwise known as kernel regression (KR). Unlike parametric models, KR is an algorithmic estimation procedure which assumes no significant parameters, and it needs no training process after its development when new observations are prepared; which is good for a system characteristic of changing due to ageing phenomenon. Although KR is used and performed excellently when applied to steady state or normal operating data, it has limitation in time-varying data that has several repetition of the same signal, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation, such as transient–start-up and shutdown mode. The major problem of KR in such a condition is that, the values of dependent variable in those points of the same value of the predictor variables assume value of the average of those dependent variable values. However, accurate estimation of the process signal can lead to the proper understanding of the equipment behaviours as well as enhance the online monitoring applications. Therefore, in this work, we proposed a modified KR model for robust signal reconstruction to resolve the setback of convectional KR.