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Shifting artificial data to detect system failures
(
John Wiley & Sons Ltd
, 2015 , Article)
Multivariate statistical process control (MSPC) is used for simultaneously monitoring several process variables. While small changes to normal operating conditions made by this system may not seriously affect the quality ...
An adaptive step-down procedure for fault variable identification
(
Taylor & Francis
, 2015 , Article)
In a process with a large number of process variables (high-dimensional process), identifying which variables cause an out-of-control signal is a challenging issue for quality engineers. In this paper, we propose an adaptive ...
An adaptive thresholding-based process variability monitoring
(
Taylor and Francis Inc.
, 2019 , Article)
In high-dimensional processes, monitoring process variability is considerably difficult due to the large number of variables and the limited number of samples. Monitoring changes in the covariance matrix of a multivariate ...
Variable Selection-based Multivariate Cumulative Sum Control Chart
(
John Wiley and Sons Ltd
, 2017 , Article)
High-dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize ...
Bayesian framework for fault variable identification
(
Taylor and Francis Inc.
, 2019 , Article)
In most manufacturing processes, identifying the faulty process variables that may lead to process changes is crucial for quality engineers and practitioners. There are several parametric procedures for identifying faulty ...