KI-Beratung Beta
Vor Ort
Merkzettel Warenkorb
Produktbild: Rotating Machinery and Signal Processing II
Band 25 Vorbesteller

Rotating Machinery and Signal Processing II Proceedings of the 3rd International Workshop on International Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, SIGPROMD2025, November 9–11, 2025, Setif, Algeria

Aus der Reihe Applied Condition Monitoring

219,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

This book offers a timely reference on signal processing methods, and their application to the field of machine diagnostics and condition monitoring. Gathering selected contributions to the 3rd International Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, SIGPROMD2025, held on...

Beschreibung

This book offers a timely reference on signal processing methods, and their application to the field of machine diagnostics and condition monitoring. Gathering selected contributions to the 3rd International Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, SIGPROMD2025, held on November 9–11, 2025, in Setif, Algeria, it discusses vibration and acoustics in mechanical systems and structures, condition monitoring in non-stationary operations, and artificial intelligence applied to machinery fault diagnosis. It also reports on advanced modeling techniques in machinery, and reliability and lifecycle management strategies. All in all, this book offers extensive information for researchers, engineers, and professionals in the fields of mechanical engineering, signal processing and diagnostics, and a good balance of theoretical models and best practices.

 

 

 

 

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

24.02.2027
Abbildungen

XII, 169 p. 91 illus., 86 illus. in color.
Herausgeber Maroua Haddar + weitere

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

24.02.2027
Abbildungen

XII, 169 p. 91 illus., 86 illus. in color.
Herausgeber
Verlag Springer
Seitenzahl

169
Maße (B/H)

15,5/23,5 cm
Sprache

Englisch
ISBN

978-3-032-38416-4
Herstelleradresse

Springer International Publishing AG
Gewerbestr. 11
6330 Cham
Schweiz
Url: www.springer.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Rotating Machinery and Signal Processing II

  • PART TITLES: Theme 1: Bearing Systems: Modeling, Signal Processing and Fault Diagnosis.

    .- An Automated Framework for Bearing Fault Diagnosis Using Frequency Features and Bayesian-Optimized Artificial Neural Networks.

    .- Data-Driven Diagnosis of Bearing Faults in Rotating Machinery Using Raw Vibration Signals and Machine Learning Models.

    .- Simulation-Based Evaluation of Cyclostationarity and VMD for Bearing Fault Diagnosis.

    .- Modeling and Simulation of Hydrodynamic Lubrication in Textured Bearing using ANSYS-Fluent.

    .- Rolling Bearing Fault Diagnosis Using a Hybrid Approach Based on Adaptive VMD, MED, and Correlation Analysis.

    .- Rolling Bearing Fault Diagnosis Using Vibration Signals and a Random Search-Optimized Artificial Neural Network.

    PART TITLES: Theme 2 : Gear Systems, Industrial Machinery and Intelligent Maintenance Technologies.

    .- Cross-Domain Gearbox Fault Diagnosis Using Wavelet Graph Neural Networks and Meta-Learning Under Few-Shot Conditions.

    .- Vector-Embedding Retrieval of Historical Maintenance Actions for Rapid Fault Diagnostics for Cement-Plant Machinery.

    .- An adaptive signal analysis method based on SK–cochleogram and FIR filter: Application for gear fault diagnosis.

    .- Optimizing SVM for Gearbox Fault Diagnosis: The Role of Bayesian Hyperparameter Tuning.

    .- Multi-Objective Optimization of Gear Geometry for Enhanced Strength and Sliding.

    .- Comparative analysis of stacked LSTM architecture models for the task of gearbox fault diagnosis.

    .- A Comparative Analysis of Machine Learning Techniques for Gearbox Fault Diagnosis Using Vibration Signals.

    .- Leveraging Transfer Learning for Efficient Fault Detection in Induction Motors Using Infrared Thermography.