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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]

Orthonormal subspace analysis (OSA) is proposed for handling the subspace decomposition issue and the principal component selection issue in traditional key performance indicator (KPI)-related process monitoring methods such as partial least squares (PLS) and canonical correlation analysis (CCA). However, it is not appropriate to apply the static OSA algorithm to a dynamic process since OSA pays no attention to the auto-correlation relationships in variables. Therefore, a novel dynamic OSA (DOSA) algorithm is proposed to capture the auto-correlative behavior of process variables on the basis of monitoring KPIs accurately. This study also discusses whether it is necessary to expand the dimension of both the process variables matrix and the KPI matrix in DOSA. The test results in a mathematical model and the Tennessee Eastman (TE) process show that DOSA can address the dynamic issue and retain the advantages of OSA. [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]

Recursive statistical process monitoring (RSPM) methods have superior monitoring performance for industrial processes, especially those with time‐varying characteristics, and have recently been studied by many researchers. However, there is no survey paper yet that summarizes and analyzes the existing RSPM methods. In this survey, approximately 60 papers related to RSPM methods are reviewed and categorized from different aspects. Existing RSPM methods are categorized using the classification criteria proposed in this study based on the ways of recursively collecting data and the stages of recursive updating. Three core algorithms that are most commonly used in RSPM methods are uniformly summarized and analyzed specifically for studying recursive computation problems in RSPM methods. Furthermore, different types of multivariate statistical process monitoring (MSPM) methods that combine recursive computation are reviewed. [ABSTRACT FROM AUTHOR]

In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents. [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]

Abstract: Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China. [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]