Fault Diagnosis of a Granulator Operating under Time-Varying Conditions Using Canonical Variate Analysis

Elena Quatrini, Xiaochuan Li, David Mba and Francesco Costantino
Additional contact information
Elena Quatrini: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy
Xiaochuan Li: Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
David Mba: Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
Francesco Costantino: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy

Energies, 2020, vol. 13, issue 17, 1-18

Abstract: Granulators play a key role in many pharmaceutical processes because they are involved in the production of tablets and capsule dosage forms. Considering the characteristics of the production processes in which a granulator is involved, proper maintenance of the latter is relevant for plant safety. During the operational phase, there is a high risk of explosion, pollution, and contamination. The nature of this process also requires an in-depth examination of the time-dependence of the process variables. This study proposes the application of canonical variate analysis (CVA) to perform fault detection in a granulation process that operates under time-varying conditions. Beyond this, a different approach to the management of process non-linearities is proposed. The novelty of the study is in the application of CVA in this kind of process, because it is possible to state that the actual literature on the theme shows some limitations of CVA in such processes. The aim was to increase the applicability of CVA in variable contexts, with simple management of non-linearities. The results, considering process data from a pharmaceutical granulator, showed that the proposed approach could detect faults and manage non-linearities, exhibiting future scenarios for more performing and automatic monitoring techniques of time-varying processes.

Keywords: condition monitoring; performance estimation; multivariate methods; pharmaceutical plant; machine learning; canonical variate analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/17/4427/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/17/4427/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:17:p:4427-:d:404820

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().