Quality Management of Data and Statistical Process Monitoring
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 1 June 2027 | Viewed by 17
Editors
Interests: statistical process monitoring; nonparametric control chart; high-dimensional process monitoring
Special Issue Information
Dear Colleagues,
In the era of big data and smart manufacturing, the quality management of data and the real-time monitoring of statistical process behavior have become indispensable for ensuring operational excellence, product consistency, and informed decision‑making. Statistical process monitoring provides a rigorous framework for detecting unusual variations and sustaining process stability. Mathematical and statistical models lie at the heart of these activities, enabling the characterization of normal process dynamics, the identification of out‑of‑control signals, and the diagnosis of assignable causes.
The scope of data quality management and statistical process monitoring spans diverse domains, including industrial production lines, healthcare systems, financial transactions, environmental surveillance, and large‑scale networked infrastructures. In each setting, tailored models are required to handle high‑dimensional, autocorrelated, or non‑normal data, as well as to accommodate missing values, outliers, and measurement errors. These models may incorporate classical control charts, multivariate statistics, time‑series analysis, Bayesian inference, and modern machine learning algorithms, all of which must be carefully designed to balance sensitivity to faults with robustness against false alarms.
Theoretical and computational investigations of these models are essential for developing efficient monitoring schemes that can operate online with minimal latency. This Special Issue invites contributions that present novel methodologies for data quality enhancement, innovative statistical process monitoring techniques, and case studies demonstrating practical impact. Particular emphasis is placed on approaches that address real‑world challenges, such as multivariate and high‑dimensional monitoring, time‑series analysis and change‑point detection and tme‑series analysis and change‑point detection. We hope to stimulate interdisciplinary dialog and advance the frontier of data‑driven quality management and process surveillance.
Dr. Anan Tang
Prof. Dr. Jinsheng Sun
Guest Editors
Manuscript Submission Information
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Keywords
- statistical process monitoring
- control chart
- fault detection
- data quality assessment and integrity
- multivariate and high dimensional monitoring
- time series analysis and change point detection
- machine learning for process surveillance
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