THESIS
2018
xiii, 112 pages : illustrations ; 30 cm
Abstract
Statistical process control (SPC) is an important and popular set of techniques for
quality improvement in various engineering applications. With the recent development
of production processes and sensing technologies, in an increasing number of modern
industrial cases, the quality characteristics of a process or product can be well summarized
by a profile which is defined as a relationship or function between a response variable
and one or a few explanatory variables. Compared to univarite scalars and multivariate
vectors, profile data convey much more complete and detailed quality information, and
have evolved to be one of the most active research areas in SPC.
This thesis is devoted to developing new SPC methodologies for profile data analytics.
Particularly, an wide array o...[
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Statistical process control (SPC) is an important and popular set of techniques for
quality improvement in various engineering applications. With the recent development
of production processes and sensing technologies, in an increasing number of modern
industrial cases, the quality characteristics of a process or product can be well summarized
by a profile which is defined as a relationship or function between a response variable
and one or a few explanatory variables. Compared to univarite scalars and multivariate
vectors, profile data convey much more complete and detailed quality information, and
have evolved to be one of the most active research areas in SPC.
This thesis is devoted to developing new SPC methodologies for profile data analytics.
Particularly, an wide array of heterogeneous profile data of unique and complicated
structures are systematically considered. Due to complex process mechanisms or diverse
measurement capabilities in modern quality applications, the profile data may be of multiple
functional forms or be collected at distinct accuracy levels. This heterogeneity of
profile data results in a broad class of new challenges which have been successfully addressed
in this thesis by the proposed novel statistical modeling and monitoring methods.
Specifically, this thesis consists of four original research essays which belong to two
categories and divide the thesis into two parts. The first part concerns the profile data
generated in the heterogeneous production processes that have multiple operating conditions.
To monitor multi-type shape profiles in the first essay, a registration-free approach
is proposed to effectively extract features from each shape type, and a Gaussian mixture
model is used to capture the heterogeneity of feature vectors. An adaptive control chart is developed to monitor the multi-modal density functions in the second essay, where the
likelihood function is incorporated with two penalties to infer the subpopulation parameters
and the number of subpopulations simultaneously. In this way, the process changes
in the model parameters as well as in the model order can be both quickly detected.
In the second part, the heterogeneous profile data result from the diverse or inconsistent
measuring capabilities of measurement systems. To cheaply and reliably collect
shape profile or surface data when both the high-end and low-end measurement devices
are available, the third essay proposes a Bayesian generative model which parameterizes
the two-resolution measurement data, based on which the accuracy of a new profile of
low-resolution can be greatly improved. In the final essay, a fast in computation and
robust to outliers kernel smoothing method is designed, and then a control chart is developed
to monitor the free-form complex surface scanning data. The four essays above
provide feasible and novel solutions to offline modeling and online monitoring of the heterogeneous
profile data, the superiority of which is verified in extensive numerical Monte
Carlo simulations as well as in real example studies.
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