Diese Seite wurde mithilfe maschineller Übersetzung übersetzt. Klicken Sie hier, um das englische Original zu sehen.

Anomalieerkennung

Signalanomalien mithilfe von KI-Modellen, einschließlich Deep-Learning-Netzwerken, erkennen.

Extrahieren Sie spärliche Zeit-Frequenz-Merkmale, um Anomalien in elektromagnetischen, akustischen und Vibrationsdaten zu erkennen.

Um Lösungen für fortgeschrittene Workflows zur Erkennung von Zeitreihenanomalien zu erkunden, siehe Time Series Anomaly Detection Support Package for MATLAB.

Funktionen

alle erweitern

deepSignalAnomalyDetectorCreate signal anomaly detector (Seit R2023a)
audioDatastoreDatastore for collection of audio files
arrayDatastoreDatastore for in-memory data
signalDatastoreDatastore for collection of signals
imageDatastoreDatastore for image data
waveletScatteringWavelet time scattering
signalTimeFeatureExtractorStreamline signal time feature extraction
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (Seit R2021b)
signalTimeFrequencyFeatureExtractorStreamline signal time-frequency feature extraction (Seit R2024a)
stftLayerShort-time Fourier transform layer (Seit R2021b)
istftLayerInverse short-time Fourier transform layer (Seit R2024a)
cwtLayerContinuous wavelet transform layer (Seit R2022b)
icwtLayerInverse continuous wavelet transform layer (Seit R2024b)
modwtLayerMaximal overlap discrete wavelet transform layer (Seit R2022b)

Verwandte Informationen

Enthaltene Beispiele

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

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

Detect Anomalies in Signals Using deepSignalAnomalyDetector

Detect Anomalies in Signals Using deepSignalAnomalyDetector

Use autoencoders to detect abnormal points or segments in time series data.

Crack Identification from Accelerometer Data

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

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)

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.

(Deep Learning Toolbox)