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ORIGINAL RESEARCH article

Front. Mech. Eng.
Sec. Vibration Systems
Volume 10 - 2024 | doi: 10.3389/fmech.2024.1493579

Diagnosis and Prognosis of Soybean Roaster Failures Using Particle Swarms

Provisionally accepted
NGNASSI DJAMI Aslain Brisco NGNASSI DJAMI Aslain Brisco 1*NGNASSI NGUELTCHEU Ulrich NGNASSI NGUELTCHEU Ulrich 1NKONGHO ANYI Joseph NKONGHO ANYI Joseph 2
  • 1 University of Ngaoundéré, Ngaoundéré, Cameroon
  • 2 University of Douala, Douala, Littoral, Cameroon

The final, formatted version of the article will be published soon.

    In industry, the monitoring and diagnosis of production processes are crucial issues in ensuring plant reliability, performance and quality. In particular, food processing operations, such as coffee roasting, are subject to numerous risks of failure that can impact on productivity and the quality of the final product. In this context, the main objective of this study was to develop an innovative method for the diagnosis and prognosis of failures in a coffee roasting process. The proposed method differs from standard approaches by using the particle swarm optimization (PSO) algorithm applied to the analysis of signatures of key process variables. This new approach has improved fault detection, with a recognition rate of over 90% for the main types of fault identified, such as heating problems, air obstructions or leaks. In addition to diagnostics, the method has also demonstrated its effectiveness in prognosticating the state of health of the process, with an average error on the prediction of remaining service life reduced to 15%, compared with 35% for fixed-threshold methods. This work has therefore enabled us to develop an innovative method offering superior performance to standard approaches for the diagnosis and prognosis of failures in the roasting process.

    Keywords: diagnosis, prognosis, Roaster, Particle swarms, Predictive maintenance

    Received: 09 Sep 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Aslain Brisco, Ulrich and Joseph. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: NGNASSI DJAMI Aslain Brisco, University of Ngaoundéré, Ngaoundéré, Cameroon

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.