Anomaly Detection

Detect signal anomalies using neural networks and autoencoders

Extract sparse time-frequency features based on wavelets to detect anomalies.

Functions

deepSignalAnomalyDetector Create signal anomaly detector (Since R2023a)

`cwtLayer`	Continuous wavelet transform layer (Since R2022b)
`icwtLayer`	Inverse continuous wavelet transform layer (Since R2024b)
`modwtLayer`	Maximal overlap discrete wavelet transform layer (Since R2022b)
`stftLayer`	Short-time Fourier transform layer (Since R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Since R2024a)

`dlcwt`	Deep learning continuous wavelet transform (Since R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (Since R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Since R2022a)
`dlstft`	Deep learning short-time Fourier transform (Since R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Since R2024a)
`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
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (Since R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Since R2021a)
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (Since R2024a)
`waveletScattering`	Wavelet time scattering

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

Wavelet Scattering	Model wavelet scattering network in Simulink (Since R2022b)
Deep Signal Anomaly Detector	Detect signal anomalies using deep learning network in Simulink (Since R2024a)

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 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.

Detect anomalies in acoustic data using wavelet scattering with the deepSignalAnomalyDetector object.

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

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

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

Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.

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

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

Detect faults in three-phase power transmission using the Deep Signal Anomaly Detector block.

(DSP System Toolbox)