Phase Division and Fault Detection Method Based on Correlation Analysis
A technology of correlation analysis and fault detection, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., which can solve problems such as reduced accuracy of representative models in stages, time sequence crossover, selection of restricted parameters, etc.
Active Publication Date: 2021-05-25
HUZHOU TEACHERS COLLEGE
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The basic idea of the cluster-based stage identification method is that the change of the industrial process relationship is related to the switching of multiple stages. This type of method can effectively capture the characteristics of the dynamic industrial process without filling unknown and missing data, but this type of method still exists. Some deficiencies, for example, the multi-stage division of the clustering algorithm cannot consider the order and timing of the stages, and it is prone to time series crossover.
In addition, the stage division algorithm ignores the stage transfer characteristics of the transition stage, and strictly divides each sample into a specific stage, which will easily lead to a decrease in the accuracy of the stage representative model, and the cluster-based stage division method is often limited by the parameters Choices such as initial center, initial number of clusters, minimum stage duration length
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[0042] In the present invention, partial least squares (PLS) is an important method in multivariate statistical analysis, which mainly focuses on finding the relationship between the multidimensional matrix X and Y, and extracting the optimal low-dimensional feature interpretation direction. is built in the sense of predictive power from the input space to the output space. Multidirectional Partial Least Squares (MPLS) is an extension of PLS, by combining multiple batches of historical industrial process data matrix X (I×J×K) and quality data matrix Y (I×M×K) with variables Or batch-wise expanded into two-dimensional matrices X(IK×J) and Y(IK×M), and extract low-dimensional latent features. The above expansion method not only preserves the nonlinear time-varying trajectory between variables, but also preserves the average trajectory between batches. Among them, the monitoring method based on the variable expansion method neither needs forecast data nor requires batches of equ...
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The invention relates to a phase division and fault detection method based on correlation analysis. The method utilizes the characteristics of the information change degree in the time slice matrix of the stable stage and the transition stage of the industrial process, etc., and conducts multiple analysis of the reaction duration of the entire industrial process. stage identification. The method first arranges and standardizes the historical batch data according to the variable expansion method. Secondly, the expansion is carried out in batches, and the expanded time slice matrix contains the continuous time-varying characteristics of the industrial process, and the time series phases are divided according to the distribution characteristics of the evaluation value. After the stages are divided, a model is established in each time-continuing stage to monitor quality-related faults, extract residual information subspace for industrial process variables, and monitor industrial process and quality-related anomalies. The application of this method to industrial penicillin fermentation process shows that the proposed method has better monitoring performance and prediction ability.
Description
technical field [0001] The invention belongs to the technical field of automatic control, and relates to a phase division and fault detection method based on correlation analysis. Background technique [0002] The performance monitoring of industrial processes generally starts from historical production data, establishes corresponding performance monitoring and fault detection models through statistical information processing methods, and uses this model to monitor the industrial process of product production, discover and eliminate abnormalities in industrial processes in time conditions, so that the industrial process of production can run efficiently, safely and stably. Traditional analytical model-based production industrial process monitoring requires accurate mathematics and production experience, which limits its practical application. Multi-way Principle Component Analysis (MPCA) and multi-way partial least squares (Multi-way partial Least Squares, MPLS) are commonl...
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IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 王培良叶晓丰杨泽宇
Owner HUZHOU TEACHERS COLLEGE