Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme

DOI10.1080/0020754032000123614zbMath1053.93038OpenAlexW1981400526MaRDI QIDQ4826249

H. Brian Hwarng

Publication date: 11 November 2004

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/0020754032000123614

zbMATH Keywords

neural networksautocorrelationback-propagation algorithmdelta-bar-delta learning rule

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Neural networks for/in biological studies, artificial life and related topics (92B20) Detection theory in information and communication theory (94A13) Stochastic systems in control theory (general) (93E03) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items

Shift detection and source identification in multivariate autocorrelated processes ⋮ Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach ⋮ Toward identifying the source of mean shifts in multivariate SPC: a neural network approach ⋮ Adaptive resonance theory-based neural algorithms for manufacturing process quality control ⋮ Simultaneous identification of mean shift and correlation change in AR(1) processes ⋮ A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes ⋮ Monitoring feedback-controlled processes using adaptiveT²schemes ⋮ Using anMQEchart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes

Cites Work

• Control Charts in the Presence of Data Correlation
• Performance of CUSUM Control Schemes for Serially Correlated Observations
• A neural fuzzy control chart for detecting and classifying process mean shifts
• Run-Length Distributions of Special-Cause Control Charts for Correlated Processes
• A Statistical Control Chart for Stationary Process Data
• X-bar andRcontrol chart interpretation using neural computing
• An evaluation of forecast-based quality control schemes