Abstract
The control charts with the Principal Component Analysis (PCA) approach and its extension are among the data-driven methods for process monitoring and the detection of faults. Industrial processing data involves complexities such as high dimensionality, auto-correlation, and non-stationary which may occur simultaneously. An efficient fault detection technique is an approach that is robust against data training, sensitive to all the feasible faults of the process, and agile to the detection of the faults. To date, approaches such as the recursive PCA (RPCA) model and the moving-window PCA (MWPCA) model have been proposed when data is high-dimensional and non-stationary or dynamic PCA (DPCA) model and its extension have been suggested for autocorrelation data. But, using the techniques listed without considering all aspects of the process data increases fault detection indicators such as false alarm rate (FAR), delay time detection (DTD), and confuses the operator or causes adverse consequences. A new PCA monitoring method is proposed in this study, which can simultaneously reduce the impact of high-dimensionality, non-stationary, and autocorrelation properties. This technique utilizes DPCA property to decrease the effect of autocorrelation and adaptive behavior of MWPCA to control non-stationary characteristics. The proposed approach has been tested on the Tennessee Eastman Process (TEP). The findings suggest that the proposed approach is capable of detecting various forms of faults and comparing attempts to improve the detection of fault indicators with other approaches. The empirical application of the proposed approach has been implemented on a turbine exit temperature (TET). The results demonstrate that the proposed approach has detected a real fault successfully.
Highlights
dynamic PCA (DPCA) is an extended Principal component analysis (PCA) model that can decrease the effect of autocorrelation, but like conventional PCA, it has fix thresholds which increases evaluation indicators such as false alarm rate, delaying time detection and missing detection rate
The adaptive PCA models such as recursive PCA (RPCA) and moving-window PCA (MWPCA) can cope with some kinds of nonstationary data, but due to disregarding the effect of autocorrelation in the data, evaluation indicators do not show good performance, which confuses the operator to make the right decision that may cause undesirable consequences
We proposed an improved PCA method which is a combination of DPCA and MWPCA method properties which can resolve non-stationary and autocorrelation features, so that the time lag of each variable can differ
Summary
The forgetting factor cannot be selected without prior knowledge of likely fault conditions when older samples are given down-weight Another approach proposed to cope with the non-stationary problem is moving window PCA (MWPCA) [11, 15]. MWPCA based on the application delay as V-stepahead prediction was implemented to solve this problem This method is applied using a model calculated at time t to predict the behavior of the system at time t + V and to detect the possible faults. As far as DPCA model is concerned, it can resolve auto-correlation, but it has fixed thresholds and when data is non-stationary, indicators such as false alarm rate (FAR), missed detection rate (MDR), and delay time detection (DTD) may suggest that it does not perform well The methods such as the MWPCA model can dominate the non-stationary problem with adaptive thresholds.
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