کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5132252 | 1491517 | 2017 | 10 صفحه PDF | دانلود رایگان |

- 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.
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 162, 15 March 2017, Pages 94-103