Abstract
A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed for traditional process monitoring methods which often adopt global model of data and ignore local information of data. Firstly, the training data set of each mode is partitioned by the complete link algorithm and the multivariate data space is divided into several subblocks. Then, the projection nonnegative matrix factorization (PNMF) algorithm is used to model each subspace of each mode separately. A joint probabilistic statistic index is defined to identify the running modes of the process data. Finally, the Bayesian information criterion (BIC) is used to synthesize the statistics of each subblock and construct a new statistic for process monitoring. The proposed process monitoring method is applied to the TE process to verify its effectiveness.
Highlights
With the rapid development of computer technology, the chemical process has become more automatic and intelligent
A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed
Combining the advantages of block modeling and projection nonnegative matrix factorization (PNMF) algorithm, a multimode process monitoring method based on MPNMF is proposed
Summary
With the rapid development of computer technology, the chemical process has become more automatic and intelligent. It is assumed that the MSPM method under a single working condition cannot meet the requirements of multimode monitoring This weakens the statistical characteristics of the process in different modes and leads to inaccurate process performance analysis. Considering the imperfection of process data, Li et al proposed a method based on robust nonnegative matrix projection (RNMP) to detect and diagnose faults in industrial processes [18]. Wang et al proposed an adaptive partitioned nonnegative matrix factorization algorithm based on nonfixed subblock NMF model for fault monitoring in chemical processes [23]. They considered the local information of the data, they did not consider the mode identification of multimode processes. This paper uses the PNMF method to extract the main characteristics of the data and establish a monitoring model, which expands the application of PNMF in the field of process monitoring
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