Results 221 to 230 of about 2,332,068 (309)

Dynamic statistical process monitoring based on online dynamic discriminative feature analysis

Journal of Process Control, 2021
A novel online discriminative dynamic feature analysis (ODDFA) algorithm is formulated and then employed for dynamic process monitoring. Different from traditional multivariate analytical algorithms which derive representative signature inherited in a ...
Shanzhi Li, Chudong Tong, Yang Chen, Ting Lan   +3 more
semanticscholar   +1 more source

A review of machine learning kernel methods in statistical process monitoring

Computers & industrial engineering, 2020
The complexity of modern problems turns increasingly larger in industrial environments, so the classical process monitoring techniques have to adapt to deal with those problems.
Anastasios Apsemidis, S. Psarakis, Javier M. Moguerza   +2 more
semanticscholar   +1 more source

Robust statistical process monitoring

Computers & Chemical Engineering, 1996
Abstract 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
openaire   +1 more source

STATISTICAL PROCESS MONITORING WITH PRINCIPAL COMPONENTS

Quality and Reliability Engineering International, 1996
Most 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, George C. Runger, Douglas C. Montgomery   +2 more
openaire   +1 more source

Modified non-Gaussian multivariate statistical process monitoring based on the Gaussian distribution transformation

, 2020
Independent component analysis (ICA) has been applied for non-Gaussian multivariate statistical process monitoring (MSPM) for several years. As the independent components do not satisfy the multivariate Gaussian distribution, a missed alarm occurs when ...
Wenyou Du, Ying-wei Zhang, Wei Zhou
semanticscholar   +1 more source

Statistical Process Monitoring With MTConnect

ASME 2012 International Manufacturing Science and Engineering Conference, 2012
Statistical 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
openaire   +1 more source

Statistical process monitoring by using process mining

IEEE Conference Anthology, 2013
There is well known: whether the advantage of using a normal process model to monitor the stability of a manufacturing process can be gained lies in the model's ability used to realize its conformance to the manufacturing process trend. In other words, whether a manufacturing process can be stabilized depends on how much is about the conformance level ...
null Ze-Crong Haung, null Yun-Shiow Chen, null Yun-Kung Chung   +2 more
openaire   +1 more source

Canonical correlation analysis-based explicit relation discovery for statistical process monitoring

Journal of the Franklin Institute, 2020
Different from the latent variables which characterize the implicit relation, the proposed method focuses on discovery and description of the explicit relation between measured variables, based on which a novel statistical process monitoring approach is ...
Shengjun Meng, Chudong Tong, Ting Lan, Haizhen Yu   +3 more
semanticscholar   +1 more source

Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice

Technometrics
Industry 4.0 has emerged as an important era for process monitoring and improvement. Our expository paper provides a historical perspective on research and practice of statistical process monitoring (SPM) from the 1920s to the present to bring a high ...
B. Colosimo, L. A. Jones‐Farmer, F. Megahed, K. Paynabar, Chitta Ranjan, W. Woodall   +5 more
semanticscholar   +1 more source

Statistical process monitoring as a big data analytics tool for smart manufacturing

Journal of Process Control, 2017
With ever-accelerating advancement of information, communication, sensing and characterization technologies, such as industrial Internet of Things (IoT) and high-throughput instruments, it is expected that the data generated from manufacturing will grow ...
Q. He, Jin Wang
semanticscholar   +1 more source