Bruit, vibrations et rudesse
Identifiez les paramètres modaux et les fonctions de réponse en fréquence pour l’analyse modale. Effectuez des analyses vibratoires et de fatigue avec le comptage Rainflow et les spectres d’enveloppes. Détectez les anomalies avec l’extraction de caractéristiques multi-domaines, la diffusion en ondelettes et les modèles de Deep Learning.
Informations connexes
Sélection d՚exemples
Vibration Analysis of Rotating Machinery
Analyze vibration signals from a gearbox using time-synchronous averaging and envelope spectra.
Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects
Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.
Modal Analysis of a Simulated System and a Wind Turbine Blade
Estimate frequency-response functions and modal parameters from experimental data.
Practical Introduction to Fatigue Analysis Using Rainflow Counting
Use rainflow counting to perform fatigue analysis and find the total damage on a mechanical component due to cyclic stress.
Compute Envelope Spectrum of Vibration Signal
Compute the envelope spectrum of a signal and combine app-generated scripts and functions into a single workflow.
Practical Introduction to Shock Waveform and Shock Response Spectrum
Compute shock response spectra of synthetic and measured transient acceleration signals.
Detect Anomalies in Machinery Using LSTM Autoencoder
Use a long short-term memory autoencoder to detect anomalies in data from an industrial machine.
Crack Identification from Accelerometer Data
Use wavelet and deep learning techniques to detect and localize transverse pavement cracks.
Detect Anomalies Using Wavelet Scattering with Autoencoders
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.
Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and the
deepSignalAnomalyDetector object.
Fault Detection Using Wavelet Scattering and Recurrent Deep Networks
Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.
