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- W2897893610 abstract "In the multi-period batch process, there are many time periods, and the fuzzy transition areas corresponding to the process characteristics of adjacent areas from one period to another cannot be strictly divided. If it is impossible to accurately classify the transition period, misjudgment is likely to occur when using the multi-way principal component analysis (MPCA) method. In order to make the fault diagnosis of the complex system in industrial production more accurate, this paper proposes a method based on the best clustering of k-means to divide the soft period. On the basis of the traditional phase division, this method adds the theory of K-means best cluster phase transition interval screening. And this method integrates the soft-time segmentation algorithm to improve the reliability of the sub-period division so as to make the transition period more accurate. The effectiveness of the method is proved by the simulation experiment of machine tool processing data and by compared with other methods." @default.
- W2897893610 created "2018-10-26" @default.
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- W2897893610 date "2018-07-01" @default.
- W2897893610 modified "2023-09-26" @default.
- W2897893610 title "Transition-based Soft Phase Partition Algorithm of Multiphase Batch Processes Based on K-means Optimal Clustering" @default.
- W2897893610 doi "https://doi.org/10.23919/chicc.2018.8482829" @default.
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