Decentralized process monitoring method

Abstract

The invention relates to a decentralized process monitoring method, which comprises the steps as follows: in step one, data is acquired; in step two, acquired data is processed through blocking partition; in step three, partitioned data is mapped to feature space; in step four, a systematic process is processed through modeling; and in step five, failure detection and identification are performed by utilizing principal component statistic T<2> of observed data and residual error statistic SPE of observed data. The decentralized process monitoring method has the advantages as follows: the method is suitable for a complex large-scale systematic process; the advantages of kernel partial least square method and blocking partial least square method can be achieved; the complexity of nonlinear process analysis is reduced at the same time; and the identification capability is enhanced.

Description

technical field [0001] The invention belongs to the technical field of fault detection, and in particular relates to a decentralized process monitoring method. Background technique [0002] With the rapid development of computer and electronic measurement technology, most modern industrial processes have complete sensing and measuring devices, and a large amount of process data can be obtained online. Statistical analysis of these data can help operators discover process faults in time, thereby reducing downtime, optimizing equipment operation, and avoiding major accidents, which promotes the research on process monitoring methods based on data analysis. At the same time, traditional fault detection approaches are model-based, so to use these methods, a deep understanding of the process must first be carried out, while data-based methods do not need to use an analytical model of the process. At present, the most used methods of this type are principal component analysis (PC...

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Problems solved by technology

[0003] The patent application for "A Non-Linear Process Fault Diagnosis Method" mainly uses the core principal component analysis method and the independent element analysis method to detect and diagnose faults. This detection and diagnosis method simplifies the amount of calculation, and its detection accuracy is not high. High, and the nonlinear optimization process is required for nonlinear analysis. At the same time, when dealing with complex large-scale systems, due to the large number of variables, when the variables are concentrated in one block, the interpretability of the model is weakened, and the fault detection ability is reduced. , at the same time, it is not suitable to identify the fault, which brings difficulties to fault diagnosis. The method proposed by the present invention can solve the above problems

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