Rotating Machinery and Signal Processing (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
133
Utgivningsdatum
2018-12-28
Upplaga
Softcover reprint of the original 1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Chaari, Fakher / Haddar, Mohamed
Illustrationer
84 Illustrations, black and white; VIII, 133 p. 84 illus.
Dimensioner
234 x 156 x 8 mm
Vikt
213 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030071493

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Rotating Machinery and Signal Processing (häftad)

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

av Ahmed Felkaoui, Fakher Chaari, Mohamed Haddar
Häftad Engelska, 2018-12-28
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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.
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Ahmed Felkaoui, Fakher Chaari, Mohamed Haddar

Innehållsförteckning

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.