Reports on advanced methods for gearbox failure detection
Describes applications of artificial neural networks, time-frequency filtering and support vector machines
Discusses physical and mathematical fundamentals
Highlights industrially-relevant applications
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.
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.
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.
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.
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.
Klappentext
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.
Reports on advanced methods for gearbox failure detection
Describes applications of artificial neural networks, time-frequency filtering and support vector machines
Discusses physical and mathematical fundamentals
Highlights industrially-relevant applications
