A New Distribution-free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions

2013 ◽  
Vol 30 (2) ◽  
pp. 191-204 ◽  
Author(s):  
S. Chowdhury ◽  
A. Mukherjee ◽  
S. Chakraborti
Keyword(s):  
Control Chart ◽  
Continuous Distributions ◽  
Distribution Free ◽  
Scale Parameters ◽  
Joint Monitoring ◽  
New Distribution

scholarly journals Distribution-free Phase II CUSUM Control Chart for Joint Monitoring of Location and Scale

2014 ◽  
Vol 31 (1) ◽  
pp. 135-151 ◽  
Author(s):  
Shovan Chowdhury ◽  
Amitava Mukherjee ◽  
Subhabrata Chakraborti
Keyword(s):  
Control Chart ◽  
Phase Ii ◽  
Distribution Free ◽  
Cusum Control Chart ◽  
Joint Monitoring

Diagnosing Problems of Distribution-Free Multivariate Control Chart

2014 ◽  
Vol 971-973 ◽  
pp. 1602-1606
Author(s):  
Wen Li Shi ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Historical Data ◽  
Important Application ◽  
Distribution Free ◽  
On Line ◽  
Multivariate Control Chart ◽  
Diagnostic Framework ◽  
Multivariate Control ◽  
Control Limits ◽  
New Distribution

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

A New Distribution-Free Control Chart for Monitoring Process Median Based on the Statistic of the Sign Test

2021 ◽  
Vol 50 (1) ◽  
pp. 20210135
Author(s):  
Saber Ali ◽  
Zameer Abbas ◽  
Hafiz Zafar Nazir ◽  
Muhammad Riaz ◽  
Muhammad Abid
Keyword(s):  
Control Chart ◽  
Sign Test ◽  
Distribution Free ◽  
Monitoring Process ◽  
New Distribution

Performance comparisons of distribution-free Shewhart-type Lepage and Cucconi schemes in monitoring complex process distributions

2020 ◽  
Vol 42 (14) ◽  
pp. 2787-2811 ◽  
Author(s):  
Zhi Lin Chong ◽  
Shuo Huang ◽  
Amitava Mukherjee ◽  
Jun Yang
Keyword(s):  
Additional Parameter ◽  
Control Performance ◽  
Monitoring And Control ◽  
Performance Study ◽  
Time To Event ◽  
Distribution Free ◽  
Scale Parameters ◽  
Scale Family ◽  
Joint Monitoring ◽  
And Control

In recent years, researchers introduced several distribution-free schemes for simultaneously monitoring the location and scale parameters of distribution in the literature related to process monitoring and control. To this end, the Shewhart-Lepage (SL) and Shewhart-Cucconi (SC) schemes are two fundamental distribution-free schemes. These schemes are primarily designed to monitor the location-scale family of densities. In practice, apart from the location and scale parameters, we often encounter the presence of a shape (or skewness) parameter. In this article, we investigate the performance of the SL and SC schemes in monitoring such models. We consider some skewed distributions in the location-scale family with one or two additional parameters, some three-parameter time-to-event processes, such as three-parameter Weibull and Gamma, which are very common in various measurement and control literature. First, we present the in-control performance of the two competing schemes and then carry out a comprehensive out-of-control performance study by considering different combinations of shifts. Several recent investigations showed that the SC scheme performs just as well or better than the SL scheme in joint monitoring of the location and scale parameters for a large number of process distributions. The current study shows that in the presence of an additional parameter, especially when the shift in the shape parameter is substantial, the SL scheme is better; for a small change in shape, the SC scheme is more competitive. In general, the SL scheme performs better in monitoring the three-parameter distributions for time-to-event processes. Finally, a real application and some concluding remarks are presented.

Sign in / Sign up

Export Citation Format

Share Document