Q residual non-parametric Distribution on Fault Detection Approach Using Unsupervised LSTM-KDE

It is well known among practitioner, majority collected data from industrial process plant are unlabeled. The collected historical data if utilize, able to provide vital information of process plant condition. Learning from unlabeled dataset, this study proposed Unsupervised LSTM-KDE approach as a measure to predict fault in industrial process plant. The residual based fault detection approach framework is utilized with long short-term memory (LSTM) as the main pattern learner for nonlinear and multimode condition that usually appear in process plant. Furthermore, kernel density approach (KDE) is used to determine the threshold value in non-parametric condition of unlabeled data. The LSTM-KDE approach later is evaluated with numerical data as well as Tennessee Eastman process plant dataset. The performance also was compared to Principal Component Analysis (PCA), Local outlier factor (LOF) and Auto-associative Kernel Regression (AAKR) to further examine the LSTM-KDE performance. The experimental results indicate that the LSTM-KDE fault detection approach has better learning performance and accuracy compared to other approaches.