Abstract:
It is important to monitor manufacturing processes in order to improve product
quality and reduce production cost. Statistical Process Control (SPC) is the
most commonly used method for process monitoring, in particular making
distinctions between variations attributed to normal process variability to
those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods
are parametric in that they make assumptions about the distributional properties
and autocorrelation structure of in-control process parameters, and, if
satisfied, are effective in managing false alarms/-positives and false-
negatives. However, when processes do not satisfy these assumptions, the
effectiveness of SPC methods is compromised. Several non-parametric control
charts based on sequential ranks of data depth measures have been proposed in
the literature, but their development and implementation have been rather slow
in industrial process control. Several non-parametric control charts based on
machine learning principles have also been proposed in the literature to
overcome some of these limitations. However, unlike conventional SPC methods,
these non-parametric methods require event data from each out-of-control process
state for effective model building. The paper presents a new non-parametric
multivariate control chart based on kernel distance that overcomes these
limitations by employing the notion of one-class classification based on support
vector principles. The chart is non-parametric in that it makes no assumptions
regarding the data probability density and only requires ‘normal’ or in-control
data for effective representation of an in-control process. It does, however,
make an explicit provision to incorporate any available data from out-of-control
process states. Experimental evaluation on a variety of benchmarking datasets
suggests that the proposed chart is effective for process mon
