26 research outputs found
Variables Sampling Plan For Correlated Data
The sampling plan for the mean for correlated data is studied. The Operating Characteristic (OC) of the variable sampling plan for mean for correlated data are calculated and compared with the OC of known σ case
Profile control charts based on nonparametric -1 regression methods
Classical statistical process control often relies on univariate
characteristics. In many contemporary applications, however, the quality of
products must be characterized by some functional relation between a response
variable and its explanatory variables. Monitoring such functional profiles has
been a rapidly growing field due to increasing demands. This paper develops a
novel nonparametric -1 location-scale model to screen the shapes of
profiles. The model is built on three basic elements: location shifts, local
shape distortions, and overall shape deviations, which are quantified by three
individual metrics. The proposed approach is applied to the previously analyzed
vertical density profile data, leading to some interesting insights.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS501 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes
Quality of machined products is often related to the shapes of surfaces
that are constrained by geometric tolerances. In this case, statistical
quality monitoring should be used to quickly detect unwanted deviations
from the nominal pattern. The majority of the literature has focused on
statistical profile monitoring, while there is little research on
surface monitoring. This paper faces the challenging task of moving from
profile to surface monitoring. To this aim, different parametric
approaches and control-charting procedures are presented and compared
with reference to a real case study dealing with cylindrical surfaces
obtained by lathe turning. In particular, a novel method presented in
this paper consists of modeling the manufactured surface via Gaussian
processes models and monitoring the deviations of the actual surface
from the target pattern estimated in phase I. Regardless of the specific
case study in this paper, the proposed approach is general and can be
extended to deal with different kinds of surfaces or profiles
On the Effectiveness of Profile Monitoring to Enhance Functional Performance of Particleboards
This paper explores connection between profile monitoring and functional performance of manufactured products. In particular, the empirical relationship between the vertical density profile of the particleboards and their functional performances (the internal bond and the surface soundness) is studied. Results based on a real case study showed that the profile shape clearly affects the final performance of the panel, and thus profile monitoring is really worth to keep the final quality of the product at its target level. This result motivates the second objective of the paper, which consists of comparing performance of two (parametric and nonparametric) approaches for vertical density profile monitoring
A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes
Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.11Ysciescopu
On monitoring of multiple non-linear profiles
Most state-of-the-art profile monitoring methods involve studies of one profile. However, a process may contain several sensors or probes that generate multiple profiles over time. Quality characteristics presented in multiple profiles may be related multiple aspects of product or process quality. Existing charting methods for simultaneous monitoring of each multiple profile may result in high false alarm rates. Or worse, they cannot correctly detect potential relationship changes among profiles. In this study, we propose two approaches to detect process shifts in multiple non-linear profiles. A simulation study was conducted to evaluate the performance of the proposed approaches in terms of average run length under different process shift scenarios. Pros and cons of the proposed methods are discussed. A guideline for choosing the proposed methods is introduced. In addition, a hybrid method combining the salient points of both approaches is explored. Finally, a real-world data-set from a vulcanisation process is used to demonstrate the implementation of the proposed methods
Statistical monitoring of functional data using the notion of Fr\'echet mean combined with the framework of the deformation models
The aim of this paper is to investigate possible advances obtained by the
implementation of the framework of Fr\'echet mean and the generalized sense of
mean that it offers, in the field of statistical process monitoring and
control. In particular, the case of non-linear profiles which are described by
data in functional form is considered and a framework combining the notion of
Fr\'echet mean and deformation models is developed. The proposed monitoring
approach is implemented to the intra-day air pollution monitoring task in the
city of Athens where the capabilities and advantages of the method are
illustrated.Comment: 31 pages, 11 figure