Development of a transient signal validation technique via a modified kernel regression model - 365
The demand for robust and resilient performance has led to the use of online-monitoring and signal validation techniques to monitor the process parameters and equipment conditions using several empirical models. One of these models is kernel regression (KR). Although KR is being used and performed excellently when applied to steady state, it has limitation in time-varying data that has several repetition of the same data point of the signals, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation during normal states, such as transient - start-up and shutdown mode. 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. In the sight of foregoing, this paper proposed a modified KR model, especially for transient signal validation to resolve the setback of conventional KR by improving the measure of similarities in the conventional KR. The performance of the proposed model is verified using both experimental data and the plant simulation data, and its results are compared with that of conventional KR. The results show that the proposed model has a greater robustness in particular situations compared to conventional KR, and it can be used to improve process and equipment monitoring applications. (authors)