Details

Condition Monitoring in Non-Stationary Operations.- Extraction of Weak Bearing Fault Signatures from Non-Stationary Signals Using Parallel Wavelet Denoising.- Neighbor Retrieval Visualizer for Monitoring Lifting Cranes.- Monitoring and Diagnostic Systems.- Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration.- Monitoring of a High-Speed Train Bogie Using the EMD Technique.- Default Detection in a Back-To-Back Planetary Gear-Box through Current and Vibration Signals.- Noise and Vibration in Machines.- Identification of Torsional Vibration Modal Parameters: Application on a Ferrari Engine Crankshaft.- Experimental Characterization of Metal-Mesh Isolators Damping Capacity by Constitutive Mechanical Model.- Signal Processing.- Separation of Impulse from Oscillation for Detection of Bearing Defect in the Vibration Signal.- Vibro-Acoustic Diagnosis of Machinery.- Cyclo-Non-Stationary Based Bearing Diagnostics of Planetary Gearboxes.- Cyclostationary Approach for Long Term Vibration Data Analysis.- Monitoring of Soil Density During Compaction Processes.

This book is aimed at researchers, industry professionals and students interested in the broad ranges of disciplines related to condition monitoring of machinery working in non-stationary conditions. Each chapter, accepted after a rigorous peer-review process, reports on a selected, original piece of work presented and discussed at the International Conference on Condition Monitoring of Machinery in Non-stationary Operations, CMMNO’2018, held on June 20 – 22, 2018, in Santander, Spain. The book describes both theoretical developments and a number of industrial case studies, which cover different topics, such as: noise and vibrations in machinery, conditioning monitoring in non-stationary operations, vibro-acoustic diagnosis of machinery, signal processing, application of pattern recognition and data mining, monitoring and diagnostic systems, faults detection, dynamics of structures and machinery, and mechatronic machinery diagnostics.

Reports on the latest research and industrial case studies Describes advanced signal processing methods for the analysis of non-stationary processes Covers a wide range of models, including dynamic, neural networks and probabilistic models