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이상 감지
R2026a희소화된 시간-주파수 특징을 추출하여 전자기 데이터, 음향 데이터 및 진동 데이터에서 이상을 감지합니다.
함수
블록
 | Wavelet Scattering | Simulink에서 웨이블릿 산란 신경망 모델링 (R2022b 이후) |
 | Deep Signal Anomaly Detector | Detect signal anomalies using deep learning network in Simulink (R2024a 이후) |
관련 정보
추천 예제
Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering with the
deepSignalAnomalyDetector object.
Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use a learning-based tool to detect abnormal points or segments in time series data.
Crack Identification from Accelerometer Data
Use wavelet and deep learning techniques to detect and localize transverse pavement cracks.
Detect Anomalies Using Wavelet Scattering with Autoencoders
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.
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.
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering
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
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
Fault Detection and Localization in Three-Phase Power Transmission Using Deep Signal Anomaly Detector in Simulink
Detect faults in three-phase power transmission using the Deep Signal Anomaly Detector block.
