کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5132252 1491517 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process
چکیده انگلیسی


- Models with tensor factorization can avoid destroying internal data structures.
- Local feature information is fully extracted.
- Characteristics of space and time can be considered at the same time.

Batch process data has close relation in time series and dynamic characteristics. Traditional diagnosis algorithms often ignore process correlation of time series and dynamic characteristics, which would lead to larger errors of monitoring results. Aiming at dynamic characteristics of batch process, a tensor dynamic neighborhood preserving embedded (TDNPE) algorithm is proposed in this paper. Firstly, batch process data is regarded as a kind of second order tensor. The tensor factorization method is used to model that can avoid destroying internal structures of data. Then dynamic neighborhood preserving embedded algorithm is used to extract process feature information by considering local features of space and time in tensor space and that can effectively deal with process dynamic characteristics. The contribution plot method is used to diagnose fault variables when faults are detected. The simulation results of penicillin fermentation process verified the effectiveness of the proposed algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 162, 15 March 2017, Pages 94-103
نویسندگان
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