Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions

Abstract

Highlights> Data density has been used in multivariate statistical process control (MSPC).> Data density considers nonlinearities between process variables and multimodal data distributions.> We diagnosed process variables that contribute to faults using a data density-based MSPC model. > The index uses the partial derivative of an MSPC model with respect to each variable. > The performance is confirmed with simulated datasets and a real plant dataset. AbstractMultivariate statistical process control (MSPC) is an important means of monitoring multiple process variables and their interrelationships while controlling chemical and industrial plants efficiently and stably. To consider nonlinearities between process variables and multimodal data distributions, the data density can be used as an index for fault detection. Data domains with a low data density are considered abnormal states. However, after fault detection, faulty process variables cannot be diagnosed with an MSPC model based on the data density. Therefore, we have developed a new index to diagnose the process variables that contribute to process faults using a data density-based MSPC model. The proposed index uses the partial derivative of an MSPC model with respect to each process variable. We demonstrate the effectiveness of the proposed method using numerical simulation data, Tennessee Eastman process data, and real plant data analyses.

Nonlinear Processes Fault Identification with Application to PCFBP

“…Fan and Wang (2014) proposed a kernel dynamic independent component analysis (KDICA) method and a non-linear contribution plot for monitoring a non-linear non-Gaussian dynamic process. Kaneko and Funatsu (2015) proposed a new index to diagnose the process variables that contribute to process faults using a data density-based MSPC model.…”