Low-cost Fault Diagnosis of Pneumatic Systems with Exergy and Machine Learning:
Author:
Affiliation:
1. Dalian Maritime University
Publisher
The Japan Fluid Power Systems Society
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jfpsij/16/2/16_24/_pdf
Reference17 articles.
1. 1) Mallia, J., Francalanza, E., Xuereb, P., and Refalo, P.: Intelligent Approaches for Anomaly Detection in Compressed Air Systems: A Systematic Review, Machines, vol. 11, 750 (2023), https://doi.org/10.3390/machines11070750
2. 2) Coanda, P., Avram, M., Comeaga, D., Gramescu, B., Constantin, V., and Nita, E.: A Review of Fault Detection Methods in Smart Pneumatic Systems and Identification of Key Failure Indicators. In: Machado, J., et al. Innovations in Mechatronics Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. pp. 132-142 (2022), https://doi.org/10.1007/978-3-031-09385-2_12
3. 3) Borg, M., Refalo, P., and Francalanza, E.: Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review, Energies, vol. 16, 3188 (2023), https://doi.org/10.3390/en16073188
4. 4) Gauchel, W., Streichert, T., and Wilhelm, Y.: Predictive maintenance with a minimum of sensors using pneumatic clamps as an example, 12th International Fluid Power Conference, vol. 2, pp. 175-183 (2020), https://doi.org/10.25368/2020.81
5. 5) Zhang, K.: Fault Detection and Diagnosis for Multi-Actuator Pneumatic Systems, PhD thesis, Stony Brook University (2011)
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