Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Tran, Kim Phuc

Springer Nature Switzerland AG

08/2022

269

Mole

Inglês

9783030838218

15 a 20 dias

427

Descrição não disponível.
Anomaly Detection in Manufacturing.- EWMA Time-Between-Events-and-Amplitude Control Charts for Correlated Data.- An Adaptive Exponentially Weighted Moving Average Chart for the Ratio of Two Normal Variables.- On the Performance of CUSUM t Chart in the Presence of Measurement Errors.- The Effect of Autocorrelation on the Shewhart Control Chart for the Ratio of Two Normal Variables.- LSTM Autoencoder Control Chart for Multivariate Time Series Data.- Real-Time Production Monitoring Approach for Smart Manufacturing with Artificial Intelligence Techniques.- Anomaly Detection in Graph with Machine Learning.- Profile Control Charts Based on Support Vector Data Description.- An Anomaly Detection Approach Based on the Combination of LSTM Autoencoder and Isolation Forest for Multivariate Time Series Data.
Control Charts;Machine Learning;Anomaly Detection;Statistical Quality Control;One-class Classification;Statistical Process Monitoring;Data Mining;Smart Manufacturing;Manufacturing Processes;Failure Prediction