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Data driven health monitoring of Peltier modules using machine-learning-methods

  • Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.

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Metadaten
Author:B.S. Paul Figueroa Cotorogea, Giuseppe Marino, Stefanie VoglORCiDGND
DOI:https://doi.org/10.1016/j.slast.2022.07.002
ISSN:2472-6303
Parent Title (English):SLAS Technology
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2022
Release Date:2023/12/14
Tag:Condition monitoring; Peltier modules; Polymerase-chain-reaction runs; Predictive maintenance; Supervised machine learning
Volume:27
Issue:5
First Page:319
Last Page:326
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Produktion und Systeme
Licence (German):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International