Statistical Quality Control In High Reliability Systems
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Statistical Quality Control in High Reliability Systems
"This book addresses two key issues in modern manufacturing, the selection of the best statistical quality control charts to use, and the use of buffer inventories after a process to further reduce the cost of quality. The book develops cost minimization algorithms which are applied to the Shewhart c, Cumulative Sum and Geometric Moving Average control charts. The cost performance of these charts is studied as the overall quality of a manufacturing system increases. The c chart is the less expensive chart at relatively low quality levels, and the Cumulative Sum chart surpasses both the c and Geometric Moving Average chart at medium and high quality levels."--Pref.
Frontiers in Statistical Quality Control
Author: Hans-Joachim Lenz
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
Like the first three volumes, published in 1981, 1984 and 1987 and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under three main headings; sampling inspection, process quality control and experimental design. In the first group there are nine papers on acceptance sampling. The second large group of papers deal with control charts and process control and the third group of papers includes contributions on experimental design.
Mathematical and Statistical Models and Methods in Reliability
Author: V.V. Rykov
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-11-02
The book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by renowned experts in the field of reliability, the contributions cover a wide range of applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence.