التفاصيل البيبلوغرافية
| العنوان: |
Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring |
| المؤلفون: |
Li S(李帅), Zhou XF(周晓锋), Shi HB(史海波), Pan FC(潘福成) |
| سنة النشر: |
2021 |
| المجموعة: |
Shenyang Institute Of Automation ,Chinese Academy Of Sciences: SIA OpenIR / 中国科学院沈阳自动化研究所机构知识库 |
| مصطلحات موضوعية: |
Bayesian inference, Dynamic non-Gaussian hybrid serial modeling, Hybrid serial similarity factor, Multivariate non-Gaussianity evaluation, Process monitoring, Automation & Control Systems, Chemistry, Analytical, Computer Science, Artificial Intelligence, Instruments & Instrumentation, Mathematics, Interdisciplinary Applications, Statistics & Probability, INDEPENDENT COMPONENT ANALYSIS, FAULT-DETECTION, BAYESIAN METHOD, ICA-PCA, DIAGNOSIS, DIVISION |
| الوصف: |
Process monitoring has been widely used for fault detection and performance supervision in modern industrial processes. Nevertheless, hybrid characteristics including Gaussianity, non-Gaussianity and dynamic usually coexist in process variables, which brings new challenge to obtain satisfactory monitoring performance. Aiming at the hybrid characteristics problem, this paper proposes a dynamic non-Gaussian hybrid serial modeling method for industrial process monitoring. First, a multivariate non-Gaussianity evaluation method is utilized to divide industrial process variables into the Gaussian variable subspace and the non-Gaussian variable subspace. Afterwards considering the hybrid characteristics including Gaussianity, non-Gaussianity and dynamic at information level, a dynamic principal component analysis (DPCA)-dynamic independent component analysis (DICA)-based hybrid serial modeling method is presented for analyzing simultaneously the dynamic Gaussian and non-Gaussian information in each variable subspace. Subsequently, the final monitoring results are obtained using Bayesian inference and the DPCA-DICA-based hybrid serial similarity factor is proposed for fault identification. Unlike the existing methods, the proposed method analyzes simultaneously the Gaussianity, non-Gaussianity and dynamic at different levels of variable and information for improving performance. The case studies including a numerical system, the Tennessee Eastman process and a practical industrial process demonstrate its feasibility and effectiveness. |
| نوع الوثيقة: |
report |
| اللغة: |
English |
| Relation: |
Chemometrics and Intelligent Laboratory Systems; http://ir.sia.cn/handle/173321/29319 |
| الاتاحة: |
http://ir.sia.cn/handle/173321/29319 |
| Rights: |
cn.org.cspace.api.content.CopyrightPolicy@661c5095 |
| رقم الانضمام: |
edsbas.C0839ED8 |
| قاعدة البيانات: |
BASE |