Abstract
This work presents a system for Fault Detection in a Blower by Machine Learning-based Vibrational Analysis. Fault Detection System is composed of two stages. The former carries out the wavelet decomposition of the vibration signal and represents the vibration signal by the projection onto the principal components retaining 99% of the available information. The latter performs the classification by a Linear Support Vection Machine. To validate the system an experimental laboratory, where it is possible to reproduce various faults, different in intensity and in type, has been properly built. Preliminary results, even obtained on a test of limited size, are quite encouraging.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Isermann, R.: Fault-Diagnosis System. Springer, New York (2006)
Gertler, J.: Fault detection and diagnosis. In: Encyclopedia of Systems and Control, pp. 1–7. MIT Press (2013)
Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, SIAM (1992)
Baraldi, P., Cannarile, F., Di maio, F., Zio, E.: Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng. Appl. Artif. Intell. 56(1), 1–13 (2016)
Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press (1961)
Jollife, I.T.: Principal Component Analysis. Springer-Verlag (1986)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 1–25 (1995)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (USA) (2002)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2001)
Hastie, T., Tibshirani, R., Friedman, R.: The Elements of Statistical Learning, 2nd edn. Springer (2009)
Joachim, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods-Support Vector Learning, pp. 169–184. MIT Press (1999)
Acknowledgments
Vincenzo Mariano Scarrica developed part of the work, in an internship at CIRA, for his final B.Sc. dissertation in Computer Science at Parthenope University of Naples, with the joint supervision of F. Camastra, G. Diodati and V. Quaranta. F. Camastra’s research was funded by Sostegno alla ricerca individuale per il triennio 2015–17 project of Parthenope University of Naples. G. Diodati’s and V. Quaranta’s researches were developed within PRORA project SMOS (Smart-On-Board System) funded by Italian Aerospace Research Centre (CIRA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Scarrica, V.M., Camastra, F., Diodati, G., Quaranta, V. (2021). Fault Detection in a Blower by Machine Learning-Based Vibrational Analysis. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_34
Download citation
DOI: https://doi.org/10.1007/978-981-15-5093-5_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5092-8
Online ISBN: 978-981-15-5093-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)