Produktbild: Rotating Machinery and Signal Processing II
Band 25 Vorbesteller

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

201,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

25.11.2026

Abbildungen

VI, 169 p. 89 illus., 61 illus. in color.

Herausgeber

Maroua Haddar + weitere

Verlag

Springer

Seitenzahl

169

Maße (B/H)

15,5/23,5 cm

Sprache

Englisch

ISBN

978-3-032-38416-4

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

25.11.2026

Abbildungen

VI, 169 p. 89 illus., 61 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 Nature Customer Service Center GmbH
Europaplatz 3
69115 Heidelberg
DE
ProductSafety@springernature.com

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  • 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.