Speaker
Description
Statistical process control (SPC), as part of quality control, makes it possible to monitor the quality levels of products and services, detect possible anomalies, their assignable causes and, consequently, facilitate their continuous improvement. This work will present the application of various SPC tools for the control of processes such as transit through the Expanded Panama Canal or the energy efficiency and hygrothermal comfort in buildings. Depending on the degree of complexity of data, univariate, multivariate or functional data control charts will be used. Likewise, other alternatives for anomaly detection, from the perspective of classification methods, will also be shown.
References:
Carral, L., Tarrío-Saavedra, J., Sáenz, A. V., Bogle, J., Alemán, G., & Naya, S. (2021). Modelling operative and routine learning curves in manoeuvres in locks and in transit in the expanded Panama Canal. The Journal of Navigation, 74(3), 633-655.
Flores, M., Naya, S., Fernández-Casal, R., Zaragoza, S., Raña, P., & Tarrío-Saavedra, J. (2020). Constructing a control chart using functional data. Mathematics, 8(1), 58.
Remeseiro, B., Tarrío-Saavedra, J., Francisco-Fernández, M., Penedo, M. G., Naya, S., & Cao, R. (2019). Automatic detection of defective crankshafts by image analysis and supervised classification. The International Journal of Advanced Manufacturing Technology, 105, 3761-3777.
Sosa Donoso, J. R., Flores, M., Naya, S., & Tarrío-Saavedra, J. (2023). Local Correlation Integral Approach for Anomaly Detection Using Functional Data. Mathematics, 11(4), 815.
| Classification | Mainly application |
|---|---|
| Keywords | Statistical Process Control, Control charts, LOCI |