Anomaly Detection

Detect signal anomalies using neural networks and autoencoders

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

Funzioni

deepSignalAnomalyDetector Create signal anomaly detector (Da R2023a)

`cwtLayer`	Continuous wavelet transform (CWT) layer (Da R2022b)
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (Da R2022b)
`stftLayer`	Short-time Fourier transform layer (Da R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Da R2024a)

`dlcwt`	Deep learning continuous wavelet transform (Da R2022b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Da R2022a)
`dlstft`	Deep learning short-time Fourier transform (Da R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Da 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 (Da R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Da R2021a)
`waveletScattering`	Wavelet time scattering

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

Model wavelet scattering network in Simulink (Da R2022b)

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.