Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems
| العنوان: | Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems |
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| المؤلفون: | Imene Djelloul, Ibrahima dit Bouran Sidibe, Zaki Sari |
| المصدر: | Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 233:801-814 |
| بيانات النشر: | SAGE Publications, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Artificial neural network, Computer science, 020209 energy, Mechanical Engineering, media_common.quotation_subject, Significant part, 02 engineering and technology, Manufacturing systems, Fault (power engineering), Fault detection and isolation, Reliability engineering, Fault handling, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Quality (business), media_common |
| الوصف: | Fault diagnosis is becoming an important issue in industrial environment, and the accurate diagnosis is the most significant part in fault handling. This article discusses a fault detection and diagnosis problem for manufacturing systems taking into account rapid detection and speed performances of fault isolation with minimum ambiguity. However, in many complex real plants, it may not be possible to discover accurately the causes of probable faults. The accuracy of fault or fault detection by the traditional approaches is not adequate. Considering the quality effect of the learning algorithm, a new hybrid neural network approach is developed using the integration of a regression task for classification accuracy. Two models of neural networks: gradient descent and momentum & adaptive learning rate and Levenberg–Marquardt are investigated and compared. The performance of the proposed approach is evaluated using mean square error, convergence speed, and classification accuracy. The case study and experimental results are presented and discussed. A comparison with the Levenberg–Marquardt regression approach shows the importance of considering the proposed learning algorithm quality in the fault detection and diagnosis problem compared with those reported in the literature. |
| تدمد: | 2041-3041 0959-6518 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::32cd5b0d6f8d740d2e5ddb2a9e0df511Test https://doi.org/10.1177/0959651818823097Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi...........32cd5b0d6f8d740d2e5ddb2a9e0df511 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20413041 09596518 |
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