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

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....

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Published inEnergies (Basel) Vol. 13; no. 17; p. 4427
Main Authors Quatrini, Elena, Li, Xiaochuan, Mba, David, Costantino, Francesco
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
canonical variate analysis
condition monitoring
Contamination
Discriminant analysis
Environmental risk
Fault detection
Fault diagnosis
Granulation
Granulators
machine learning
multivariate methods
performance estimation
Pharmaceutical industry
pharmaceutical plant
Pharmaceuticals
Principal components analysis
Process variables
Product quality
Variables
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13174427