Rotating Machinery and Signal Processing

Proceedings of the First Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, SIGPROMD'2017, April 09-11, 2017, Setif, Algeria
Softcover Nachdruck of the original 1. Auflage 2019
ISBN: 978-3-030-07149-3
Verlag: Springer International Publishing

Buch, Englisch, Band 12, 133 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 230 g

Reihe: Applied Condition Monitoring

This book provides readers with a timely snapshot of the potential offered by and challenges posed by signal processing methods in the field of machine diagnostics and condition monitoring. It gathers contributions to the first Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, held in Setif, Algeria, on April 9-10, 2017, and organized by the Applied Precision Mechanics Laboratory (LMPA) at the Institute of Precision Mechanics, University of Setif, Algeria and the Laboratory of Mechanics, Modeling and Manufacturing (LA2MP) at the National School of Engineers of Sfax. The respective chapters highlight research conducted by the two laboratories on the following main topics: noise and vibration in machines; condition monitoring in non-stationary operations; vibro-acoustic diagnosis of machinery; signal processing and pattern recognition methods; monitoring and diagnostic systems; and dynamic modeling and fault detection.

Zielgruppe

Research

Autoren/Hrsg.

Felkaoui, Ahmed

Haddar, Mohamed

Chaari, Fakher

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Triebwerkstechnik, Energieübertragung

Inhaltsverzeichnis

From the content: Feature Selection Scheme Based on Pareto Method for Gearbox Fault Diagnosis.- A Intelligent Gear Fault Diagnosis in Normal and Non-Stationary Conditions Based on Instantaneous Angular Speed, Differential Evolution and Multi-class Support Vector Machine.- Effect of Input Data on The Neural Networks Performance Applied in Bearing Fault Diagnosis.