Results 201 to 210 of about 705,570 (265)
Measuring multisector nutrition and health intervention coverage using composite coverage analysis methods: a scoping review and methodological guidance. [PDF]
BMJ OpenMorrison T +4 more
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From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives. [PDF]
Int Neurourol JEun SJ, Park JM, Na YG.
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Statistical process monitoring: basics and beyond
Journal of Chemometrics, 2003AbstractThis paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement ...
S Joe Qin
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STATISTICAL PROCESS MONITORING WITH PRINCIPAL COMPONENTS
Quality and Reliability Engineering International, 1996Most industrial processes are characterized by a system of several variables, all of which are subject to drifts, disturbances, and assignable causes of variation. In the chemical and process industries, there are often inertial forces arising from raw material streams, reactors and tanks that introduce serial correlation over time into these variables.
Christina M. Mastrangelo +2 more
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Robust statistical process monitoring
Computers & Chemical Engineering, 1996Abstract Principal component analysis (PCA) is a key step to carrying out multivariate statistical process monitoring. Due to the sensitive nature of classical PCA, one or two outliers will cause misleading results. In this paper, a robust PCA via a Hybrid Projection Pursuit (HPP) approach is proposed.
J. Chen, A. Bandoni, J.A. Romagnoli
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Statistical Process Monitoring With MTConnect
ASME 2012 International Manufacturing Science and Engineering Conference, 2012Statistical Process Control (SPC) techniques are used widely in the manufacturing industry. However, it is sometimes observed that a deviation that is within the acceptable range of inherent process variation does not necessarily conform to specifications. This is especially true in the case of low volume; high precision manufacturing that is customary
Sri Atluru, Amit Deshpande
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PCA based statistical process monitoring of grinding process
IEEE ICCA 2010, 2010Multivariate statistical process monitoring (MSPM) has received increasing attention, which is applied to improve process operations by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. This paper presents a MSPM application method on grinding processes, including principal component analysis (PCA), fault
Lin Zhang +4 more
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Batch Statistical Process Monitoring Approach to a Cocrystallization Process
Journal of Pharmaceutical Sciences, 2015Cocrystals are defined as crystalline structures composed of two or more compounds that are solid at room temperature held together by noncovalent bonds. Their main advantages are the increase of solubility, bioavailability, permeability, stability, and at the same time retaining active pharmaceutical ingredient bioactivity.
Mafalda C, Sarraguça +3 more
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The impact of process variability on Statistical Process Monitoring
2013 Conference on Control and Fault-Tolerant Systems (SysTol), 2013Process simulators are widely used to develop and benchmark techniques for Statistical Process Monitoring (SPM). Typically, the simulators are deterministic and do not take process variability into account. However, modern processes in (bio)chemical industry focus on bio-based production with the help of microorganisms, and are, therefore, subject to ...
Geert Gins, Jef Vanlaer, Jan Van Impe
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