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Traditional multivariate statistics‐based process monitoring (MSPM) methods are static algorithms, and the "time lag shift" method (TLSM) is the most commonly used approach to handle the dynamic issue. This paper proves in theory that two drawbacks exist in TLSM‐based dynamic approaches: information unrelated to the real‐time data is also analyzed, and information that can be predicted by historical data is counted repeatedly in both real‐time and historical data. This paper adopts orthonormal subspace analysis (OSA) to handle these issues. OSA can successfully separate real‐time data into information that can be predicted by historical data (the dynamic component) and cannot be predicted for process monitoring (the static component), so the detection result is not influenced by redundant information and is more sensitive to process faults than TLSM‐based dynamic methods. [ABSTRACT FROM AUTHOR]

To overcome the shortage of traditional temperature sensors, this paper adopts infrared thermal imaging technology for temperature measurement. To avoid the spatial information loss issue during the image data vectorization process, this paper adopted the spatial relationship between pixels in principal component analysis (PCA) model training, which is called spatial information-based PCA (SIPCA). Then, spatial information is also used in the fault localization method to enhance the fault location performance. Tested by an experimental tank system, the proposed method achieves better performance than the traditional PCA approach, and it can detect heat leakage faults on the surface of the equipment. [ABSTRACT FROM AUTHOR]

Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static process monitoring. For a dynamic process, the expectation value of each variable changes over time, and hence, it cannot be replaced with a constant value. As such, the local data structure in the local kernel principal component analysis is wrong, which causes the model deviation problem. In this paper, we propose a new two-step dynamic local kernel principal component analysis, which extracts the static components in the process data and then analyzes them by local kernel principal component analysis. As such, the two-step dynamic local kernel principal component analysis can handle the nonlinearity and the dynamic features simultaneously. [ABSTRACT FROM AUTHOR]

In this study, a two-step principal component analysis (TS-PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross-correlation structure and dynamic auto-correlation structure in process data simultaneously, TS-PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS-PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS-PCA still can achieve better performance than the existing dynamic approaches. [ABSTRACT FROM AUTHOR]

To cope with the combined Gaussian and non-Gaussian features in the industrial processes, a novel preliminary-summation-based principal component analysis (PS–PCA) method is proposed in this study. Different from other approaches which improve principal component analysis (PCA) by changing its algorithm structure, PS–PCA just preprocesses the training and monitoring data without modification on PCA. According to the central limit theorem, PS–PCA adds up samples of each variable to make the distribution of the sum approach Gaussian distribution. These sums are then used for state monitoring. It has been proved that preliminary summation can increase the fault detection rate for Gaussian processes. Furthermore, some simulation tests substantiate that PS–PCA can improve the detection capability for non-Gaussian processes and even for nonlinear processes without increasing the computation load. [ABSTRACT FROM AUTHOR]

• A kurtosis function is embedded into NCA to restrict the Gaussianity of PCs. • A novel PC selection mechanism is proposed. • The gradient descent method is rederived for the INCA network. • Testing results show that INCA outperforms NCA, KPCA and O-NLPCA. Neural component analysis (NCA) is one of the latest nonlinear multivariate statistical process control (MSPC) methods, which consists in combining artificial neural networks (ANNs) with principal component analysis (PCA). However, NCA cannot handle the non-Gaussian feature and the extracted principal components (PCs) in NCA may not be the key information in the process data. Herein, we propose an improved NCA (INCA) which introduces a new cost function based on kurtosis to restrict the Gaussianity of PCs. We also propose a novel PC selection mechanism based on the information of PCs in the original data space rather than in the PC data space. INCA achieves almost 100% detection rates in three different types of faults in a simulation model test, and it can detect the fault in the thermal power plant process more than 1 min ahead of orthogonal nonlinear PCA (O-NLPCA) and NCA. [ABSTRACT FROM AUTHOR]