The Effects of Autocorrelation in the Estimation of Process Capability Indices." (1998)
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Implementing SPC for Non-Normal Processes with the I-MR Chart: a Case Study
Implementing SPC for non-normal processes with the I-MR chart: A case study Axl Elisson Master of Science Thesis TPRMM 2017 KTH Industrial Engineering and Management Production Engineering and Management SE-100 44 STOCKHOLM Acknowledgements This master thesis was performed at the brake manufacturer Haldex as my master of science degree project in Industrial Engineering and Management at the Royal Institute of Technology (KTH) in Stockholm, Sweden. It was conducted during the spring semester of 2017. I would first like to thank my supervisor at Haldex, Roman Berg, and Annika Carlius for their daily support and guidance which made this project possible. I would also like to thank the quality department, production engineers and operators at Haldex for all insight in different subjects. Finally, I would like to thank my supervisor at KTH, Jerzy Mikler, for his support during my thesis. All of your combined expertise have been very valuable. Stockholm, July 2017 Axl Elisson Abstract The application of statistical process control (SPC) requires normal distributed data that is in statistical control in order to determine valid process capability indices and to set control limits that reflects the process’ true variation. This study examines a case of several non-normal processes and evaluates methods to estimate the process capability and set control limits that is in relation to the processes’ distributions. Box-Cox transformation, Johnson transformation, Clements method and process performance indices were compared to estimate the process capability and the Anderson-Darling goodness-of-fit test was used to identify process distribution. Control limits were compared using Clements method, the sample standard deviation and from machine tool variation. -
Research Participants' Rights to Data Protection in the Era of Open Science
DePaul Law Review Volume 69 Issue 2 Winter 2019 Article 16 Research Participants’ Rights To Data Protection In The Era Of Open Science Tara Sklar Mabel Crescioni Follow this and additional works at: https://via.library.depaul.edu/law-review Part of the Law Commons Recommended Citation Tara Sklar & Mabel Crescioni, Research Participants’ Rights To Data Protection In The Era Of Open Science, 69 DePaul L. Rev. (2020) Available at: https://via.library.depaul.edu/law-review/vol69/iss2/16 This Article is brought to you for free and open access by the College of Law at Via Sapientiae. It has been accepted for inclusion in DePaul Law Review by an authorized editor of Via Sapientiae. For more information, please contact [email protected]. \\jciprod01\productn\D\DPL\69-2\DPL214.txt unknown Seq: 1 21-APR-20 12:15 RESEARCH PARTICIPANTS’ RIGHTS TO DATA PROTECTION IN THE ERA OF OPEN SCIENCE Tara Sklar and Mabel Crescioni* CONTENTS INTRODUCTION ................................................. 700 R I. REAL-WORLD EVIDENCE AND WEARABLES IN CLINICAL TRIALS ....................................... 703 R II. DATA PROTECTION RIGHTS ............................. 708 R A. General Data Protection Regulation ................. 709 R B. California Consumer Privacy Act ................... 710 R C. GDPR and CCPA Influence on State Data Privacy Laws ................................................ 712 R III. RESEARCH EXEMPTION AND RELATED SAFEGUARDS ... 714 R IV. RESEARCH PARTICIPANTS’ DATA AND OPEN SCIENCE . 716 R CONCLUSION ................................................... 718 R Clinical trials are increasingly using sensors embedded in wearable de- vices due to their capabilities to generate real-world data. These devices are able to continuously monitor, record, and store physiological met- rics in response to a given therapy, which is contributing to a redesign of clinical trials around the world. -
Overview of Reliability Engineering
Overview of reliability engineering Eric Marsden <[email protected]> Context ▷ I have a fleet of airline engines and want to anticipate when they mayfail ▷ I am purchasing pumps for my refinery and want to understand the MTBF, lambda etc. provided by the manufacturers ▷ I want to compare different system designs to determine the impact of architecture on availability 2 / 32 Reliability engineering ▷ Reliability engineering is the discipline of ensuring that a system will function as required over a specified time period when operated and maintained in a specified manner. ▷ Reliability engineers address 3 basic questions: • When does something fail? • Why does it fail? • How can the likelihood of failure be reduced? 3 / 32 The termination of the ability of an item to perform a required function. [IEV Failure Failure 191-04-01] ▷ A failure is always related to a required function. The function is often specified together with a performance requirement (eg. “must handleup to 3 tonnes per minute”, “must respond within 0.1 seconds”). ▷ A failure occurs when the function cannot be performed or has a performance that falls outside the performance requirement. 4 / 32 The state of an item characterized by inability to perform a required function Fault Fault [IEV 191-05-01] ▷ While a failure is an event that occurs at a specific point in time, a fault is a state that will last for a shorter or longer period. ▷ When a failure occurs, the item enters the failed state. A failure may occur: • while running • while in standby • due to demand 5 / 32 Discrepancy between a computed, observed, or measured value or condition and Error Error the true, specified, or theoretically correct value or condition. -
Lochner</I> in Cyberspace: the New Economic Orthodoxy Of
Georgetown University Law Center Scholarship @ GEORGETOWN LAW 1998 Lochner in Cyberspace: The New Economic Orthodoxy of "Rights Management" Julie E. Cohen Georgetown University Law Center, [email protected] This paper can be downloaded free of charge from: https://scholarship.law.georgetown.edu/facpub/811 97 Mich. L. Rev. 462-563 (1998) This open-access article is brought to you by the Georgetown Law Library. Posted with permission of the author. Follow this and additional works at: https://scholarship.law.georgetown.edu/facpub Part of the Intellectual Property Law Commons, Internet Law Commons, and the Law and Economics Commons LOCHNER IN CYBERSPACE: THE NEW ECONOMIC ORTHODOXY OF "RIGHTS MANAGEMENT" JULIE E. COHEN * Originally published 97 Mich. L. Rev. 462 (1998). TABLE OF CONTENTS I. THE CONVERGENCE OF ECONOMIC IMPERATIVES AND NATURAL RIGHTS..........6 II. THE NEW CONCEPTUALISM.....................................................18 A. Constructing Consent ......................................................18 B. Manufacturing Scarcity.....................................................32 1. Transaction Costs and Common Resources................................33 2. Incentives and Redistribution..........................................40 III. ON MODELING INFORMATION MARKETS........................................50 A. Bargaining Power and Choice in Information Markets.............................52 1. Contested Exchange and the Power to Switch.............................52 2. Collective Action, "Rent-Seeking," and Public Choice.......................67 -
Understanding Functional Safety FIT Base Failure Rate Estimates Per IEC 62380 and SN 29500
www.ti.com Technical White Paper Understanding Functional Safety FIT Base Failure Rate Estimates per IEC 62380 and SN 29500 Bharat Rajaram, Senior Member, Technical Staff, and Director, Functional Safety, C2000™ Microcontrollers, Texas Instruments ABSTRACT Functional safety standards like International Electrotechnical Commission (IEC) 615081 and International Organization for Standardization (ISO) 262622 require that semiconductor device manufacturers address both systematic and random hardware failures. Systematic failures are managed and mitigated by following rigorous development processes. Random hardware failures must adhere to specified quantitative metrics to meet hardware safety integrity levels (SILs) or automotive SILs (ASILs). Consequently, systematic failures are excluded from the calculation of random hardware failure metrics. Table of Contents 1 Introduction.............................................................................................................................................................................2 2 Types of Faults and Quantitative Random Hardware Failure Metrics .............................................................................. 2 3 Random Failures Over a Product Lifetime and Estimation of BFR ...................................................................................3 4 BFR Estimation Techniques ................................................................................................................................................. 4 5 Siemens SN 29500 FIT model -
Process Capability Analysis
6 Process capability analysis In general, process capability indices have been quite controversial. (Ryan, 2000, p. 186) Overview Capability indices are widely used in assessing how well processes perform in relation to customer requirements. The most widely used indices will be defined and links with the concept of sigma quality level established. Minitab facilities for capability analysis of both measurement and attribute data will be introduced. 6.1 Process capability 6.1.1 Process capability analysis with measurement data Imagine that four processes produce bottles of the same type for a customer who specifies that weight should lie between 485 and 495 g, with a target of 490 g. Imagine, too, that all four processes are behaving in a stable and predictable manner as indicated by control charting of data from regular samples of bottles from the processes. Let us suppose that the distribution of weight is normal in all four cases, with the parameters in Table 6.1. The four distributions of weight are displayed in Figure 6.1, together with reference lines showing lower specification limit (LSL), upper specification limit (USL) and Target (T). How well are these processes performing in relation to the customer requirements? In the long term the fall-out, in terms of nonconforming bottles, would be as shown in the penultimate column of Table 6.1. The fall-out is given as number of parts bottles) per million (ppm) that would fail to meet the customer specifications. The table in Appendix 1 indicates that these fall-outs correspond to sigma quality levels of 4.64, 3.50, 2.81 and 3.72 respectively for lines 1–4. -
Probability Analysis in Determining the Behaviour of Variable Amplitude Strain Signal Based on Extraction Segments
2141 Probability Analysis in Determining the Behaviour of Variable Amplitude Strain Signal Based on Extraction Segments Abstract M. F. M. Yunoh a This paper focuses on analysis in determining the behaviour of vari- S. Abdullah b, * able amplitude strain signals based on extraction of segments. The M. H. M. Saad c constant Amplitude loading (CAL), that was used in the laboratory d Z. M. Nopiah tests, was designed according to the variable amplitude loading e (VAL) from Society of Automotive Engineers (SAE). The SAE strain M. Z. Nuawi signal was then edited to obtain those segments that cause fatigue a damage to components. The segments were then sorted according to Department of Mechanical and Materials their amplitude and were used as a reference in the design of the Engineering, Faculty of Engineering & CAL loading for the laboratory tests. The strain signals that were Built Enviroment, Universiti Kebangsaan obtained from the laboratory tests were then analysed using fatigue Malaysia, 43600 UKM Bangi, Selangor, life prediction approach and statistics, i.e. Weibull distribution anal- Malaysia. [email protected] b ysis. Based on the plots of the Probability Density Function (PDF), [email protected] c Cumulative Distribution Function (CDF) and the probability of fail- [email protected]; d ure in the Weibull distribution analysis, it was shown that more than [email protected]; e 70% failure occurred when the number of cycles approached 1.0 x [email protected] 1011. Therefore, the Weibull distribution analysis can be used as an alternative to predict the failure probability. * Corresponding author: S. -
Individual Failure Rate Modelling and Exploratory Failure Data Analysis for Power System Components
INDIVIDUAL FAILURE RATE MODELLING AND EXPLORATORY FAILURE DATA ANALYSIS FOR POWER SYSTEM COMPONENTS REPORT 2018:549 RISK- OCH TILLFÖRLITLIGHETSANALYS Individual Failure Rate Modelling and Exploratory Failure Data Analysis for Power System Components JAN HENNING JÜRGENSEN ISBN 978-91-7673-549-7 | © Energiforsk December 2018 Energiforsk AB | Phone: 08-677 25 30 | E-mail: [email protected] | www.energiforsk.se INDIVIDUAL FAILURE RATE MODELLING AND EXPLORATORY FAILURE DATA ANALYSIS FOR POWER SYSTEM COMPONENTS Foreword Detta projekt är en fortsättning på ett doktorandprojekt från tidigare programperiod. Etappen Riskanalys III hanterade projektet från licentiat till doktorsavhandling genom att stötta en slutrapportering. Projektet studerade om felfrekvensnoggrannheten på en enskild komponentnivå kan öka trots att det tillgängliga historiska feldatat är begränsat. Resultatet var att effekterna av riskfaktorer på felfrekvens med regressionsmodeller kvantifierades, samt en ökad förståelse av riskfaktorer och högre noggrannhet av felfrekvensen. Statistisk data-underbyggda metoder används fortfarande sällan i kraftsystemen samtidigt som metoder behövs för att förbättra felfrekvensnoggrannheten trots begränsade tillgängliga data. Metoden beräknar individuella felfrekvenser. Jan Henning Jürgensen från Kungliga Tekniska Högskolan, har varit projektledare för projektet. Han har arbetat under handledning av docent Patrik Hilber och professor Lars Nordström, bägge på KTH. Stort tack till följande programstyrelse för all hjälp och vägledning: • Jenny -
Improving Process Capability Database Usage for Robust Design Engineering by Generalising Measurement Data
Downloaded from orbit.dtu.dk on: Sep 25, 2021 Improving process capability database usage for robust design engineering by generalising measurement data Okholm, A.B.; Rask, M.; Ebro, Martin ; Eifler, Tobias; Holmberg, M.; Howard, Thomas J. Published in: 13th International Design Conference - Design 2014 Publication date: 2014 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Okholm, A. B., Rask, M., Ebro, M., Eifler, T., Holmberg, M., & Howard, T. J. (2014). Improving process capability database usage for robust design engineering by generalising measurement data. In 13th International Design Conference - Design 2014 (pp. 1133-1144). Design Society. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. INTERNATIONAL DESIGN CONFERENCE - DESIGN 2014 Dubrovnik - Croatia, May 19 - 22, 2014. IMPROVING PROCESS CAPABILITY DATABASE USAGE FOR ROBUST DESIGN ENGINEERING BY GENERALISING MEASUREMENT DATA A. B. Okholm, M. Rask, M. -
Assignment 9 Control Charts, Process Capability and QFD
Assignment 9 Control Charts, Process capability and QFD Instructions: 1. Total No. of Questions: 25. Each question carries one point. 2. All questions are objective type. Only one answer is correct per numbered item. 1. How do you find the process capability? a) By Process Capability Ratio = Cp = − b) By USL and LSL only 6 c) By normal distribution curve d) By spread and mean shift of the process 2. In a certain process it is given that USL is 14, LSL is zero. The process has a mean of 10 and standard deviation 2. What will be the Process Capability Ratio? a) 0.83 b) 1.17 c) 1.33 d) 1.5 3. In a process the inverse of process capability ratio is 0.65. Which statement is correct? a) The process is capable. b) The process is incapable. c) The process is capable with tight control. d) None of the above 4. A process having mean 8.80 and process standard deviation 0.12 has spread of specification limit: 9.0±0.4. What will be the process capability index? a) 0.55 b) 1.67 c) 1.33 d) 0.83 5. How is different than ? a) Looks at the centrality of the process. b) Looks at the overall variability of the process. c) Both look at the overall variability of the process. d) Looks at the centrality of the process. 6. A QC scheme is in operation for a process producing ball-bearings. A sample of 6 bearings is taken every hour and diameters is measured. -
Use Process Capability to Ensure Product Quality
Use Process Capability to Ensure Product Quality Lawrence X. Yu, Ph.D. Director (acting) Office of Pharmaceutical Science, CDER, FDA FDA/ PQRI Conference on Evolving Product Quality September 16-17, 2104, Bethesda, MD 1 2 Quality by Testing vs. Quality by Design Quality by Testing – Specification acceptance criteria are based on one or more batch data (process capability) – Testing must be made to release batches Quality by Design – Specification acceptance criteria are based on performance – Testing may not be necessary to release batches L. X. Yu. Pharm. Res. 25:781-791 (2008) 3 ICH Q6A: Test Procedures and Acceptance Criteria… 4 5 Pharmaceutical QbD Objectives Achieve meaningful product quality specifications that are based on assuring clinical performance Increase process capability and reduce product variability and defects by enhancing product and process design, understanding, and control Increase product development and manufacturing efficiencies Enhance root cause analysis and post-approval change management 6 Concept of Process Capability First introduced in Statistical Quality Control Handbook by the Western Electric Company (1956). – “process capability” is defined as “the natural or undisturbed performance after extraneous influences are eliminated. This is determined by plotting data on a control chart.” ISO, AIAG, ASQ, ASTM ….. published their guideline or manual on process capability index calculation 7 Nomenclature Four indices: – Cp: process capability index – Cpk: minimum process capability index – Pp: process -
Chapter 2 Failure Models Part 1: Introduction
Chapter 2 Failure Models Part 1: Introduction Marvin Rausand Department of Production and Quality Engineering Norwegian University of Science and Technology [email protected] Marvin Rausand, March 14, 2006 System Reliability Theory (2nd ed), Wiley, 2004 – 1 / 31 Introduction Rel. measures Life distributions State variable Time to failure Distr. function Probability density Distribution of T Reliability funct. Failure rate Introduction Bathtub curve Some formulas MTTF Example 2.1 Median Mode MRL Example 2.2 Discrete distributions Life distributions Marvin Rausand, March 14, 2006 System Reliability Theory (2nd ed), Wiley, 2004 – 2 / 31 Reliability measures Introduction In this chapter we introduce the following measures: Rel. measures Life distributions State variable Time to failure ■ Distr. function The reliability (survivor) function R(t) Probability density ■ The failure rate function z(t) Distribution of T ■ Reliability funct. The mean time to failure (MTTF) Failure rate ■ The mean residual life (MRL) Bathtub curve Some formulas MTTF Example 2.1 of a single item that is not repaired when it fails. Median Mode MRL Example 2.2 Discrete distributions Life distributions Marvin Rausand, March 14, 2006 System Reliability Theory (2nd ed), Wiley, 2004 – 3 / 31 Life distributions Introduction The following life distributions are discussed: Rel. measures Life distributions State variable ■ The exponential distribution Time to failure ■ Distr. function The gamma distribution Probability density ■ The Weibull distribution Distribution of