Enhanced monitoring using PCA-based GLR fault detection and multiscale filtering

Abstract

One of the most popular multivariate statistical methods used for data-based process monitoring is Principal Component Analysis (PCA). In the absence of a process model, PCA has been successfully used as a data-based FD technique for highly correlated process variables. Some of the PCA detection indices include the T 2 or Q statistics, which have their advantages and disadvantages. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abilities. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The PCAbased GLR fault detection algorithm provides optimal properties by maximizing the detection probability of faults for a given false alarm rate. However, the presence of measurement noise and modeling errors increase the rate of false alarms. Therefore, to further improve the quality of fault detection, multiscale filtering is utilized to filter the residuals obtained from the PCA model, which helps suppress the effect on errors, and thus decrease the false alarm rate. The proposed fault detection methodology is demonstrated through its application to monitor the ozone level in the Upper Normandy region, France, and it is shown to effectively reduce the rate of false alarms whilst retaining the capability of detecting process faults.

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

“…The PCA algorithm was widely used in various fields of research data analysis, especially suitable for analyzing two-dimensional data matrix [38]. Researchers found that PCA could well express the basic features of the original data with less data [39]. Therefore, P300 EEG signals still retain the integrity of original EEG signals after PCA dimensionality reduction.…”

Improved data-based fault detection strategy and application to distillation columns

“…• One of the biggest advantages of multiscale representation is its capacity to distinguish measurement noise from useful data features (Harrou et al, 2013b;Madakyaru et al, 2013b) by applying low-and high-pass filters to the data during multiscale decomposition. This allows the separation of features at different resolutions or frequencies, which makes multiscale representation a better tool for filtering or denoising noisy data than traditional linear filters, like the mean filter and the exponentially weighted moving average (EWMA) filter (Sheriff et al, 2014).…”

Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

“…One of the biggest advantages of multiscale representation is its capacity to distinguish measurement noise from useful data features [53,54], by applying low and high pass filters to the data during multiscale decomposition. This allows the separation of features at different resolutions or frequencies, which makes multiscale representation a better tool for filtering or denoising noisy data than traditional linear filters, like the mean filter and the exponentially weighted moving average (EWMA) filter [48].…”