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异常检测

使用神经网络和自编码器检测信号异常

基于小波提取稀疏时频特征检测异常。

函数

自编码器

deepSignalAnomalyDetector Create signal anomaly detector （自 R2023a 起）

信号处理层

`cwtLayer`	Continuous wavelet transform (CWT) layer （自 R2022b 起）
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer （自 R2022b 起）
`stftLayer`	Short-time Fourier transform layer （自 R2021b 起）

特征提取

`dlcwt`	Deep learning continuous wavelet transform （自 R2022b 起）
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis （自 R2022a 起）
`dlstft`	Deep learning short-time Fourier transform （自 R2021a 起）
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform （自 R2019a 起）
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction （自 R2021b 起）
`signalTimeFeatureExtractor`	Streamline signal time feature extraction （自 R2021a 起）
`waveletScattering`	Wavelet time scattering

数据存储和数据管理

`edfheader`	Create header structure for EDF or EDF+ file （自 R2021a 起）
`edfinfo`	Get information about EDF/EDF+ file （自 R2020b 起）
`edfread`	Read data from EDF/EDF+ file （自 R2020b 起）
`edfwrite`	Create or modify EDF or EDF+ file （自 R2021a 起）
`paddata`	Pad data by adding elements （自 R2023b 起）
`resize`	Resize data by adding or removing elements （自 R2023b 起）
`trimdata`	Trim data by removing elements （自 R2023b 起）
`signalDatastore`	Datastore for collection of signals （自 R2020a 起）

模块

Wavelet Scattering

Model wavelet scattering network in Simulink （自 R2022b 起）

主题

Detect Air Compressor Sounds in Simulink Using Wavelet Scattering (DSP System Toolbox)
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
Detect Anomalies in ECG Time-Series Data Using Wavelet Scattering and LSTM Autoencoder in Simulink (DSP System Toolbox)
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.

精选示例

Anomaly Detection Using Autoencoder and Wavelets

Anomaly Detection Using Autoencoder and Wavelets

Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.

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Crack Identification from Accelerometer Data

Crack Identification from Accelerometer Data

Use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.

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Detect Anomalies Using Wavelet Scattering with Autoencoders

Detect Anomalies Using Wavelet Scattering with Autoencoders

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

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Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

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.

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Detect Anomalies in Signals Using deepSignalAnomalyDetector

Detect Anomalies in Signals Using deepSignalAnomalyDetector

Use a learning-based tool to detect abnormal points or segments in time-series data.

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Detect Anomalies in Machinery Using LSTM Autoencoder

Detect Anomalies in Machinery Using LSTM Autoencoder

Use a long short-term memory autoencoder to detect anomalies in data from an industrial machine.

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