噪声、振动和粗糙度

旋转机械的振动分析

使用时间同步平均法和包络频谱分析变速箱的振动信号。

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.

仿真系统和风力涡轮叶片的模态分析

根据试验数据估计频率响应函数和模态参数。

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.

(Deep Learning Toolbox)

Detect Anomalies Using Wavelet Scattering with Autoencoders

Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.

(Deep Learning Toolbox)

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.

(Deep Learning Toolbox)