ASQ RRD series webinar: Análisis de las Aplicaciones de Modelos Estadísticos para entender Covid-19

Mar., 2 de Mar. de 2021 12:00 – 13:00 EST

https://register.gotowebinar.com/register/1119714467326028304

Con la perspectiva de salud pública, Dr. Jorge Romeu va a compartir un sumario de su análisis de las contribuciones a la investigación y avance hechas por varias organizaciones que han usado modelos estadísticos con el fin de ayudar a entender Covid-19 y sus efectos. Es esta presentación Dr. Romeu presentara el estado de su análisis que ha conducido de Marzo 4 2020 a Febrero 3, 2021. Dr Romeu propone incrementar el uso de herramientas estadísticas en la profesión e investigación de la salud pública.

Jorge Luis Romeu es estadístico industrial especializado en Calidad y Confiabilidad. Es Profesor Emerito de la Universidad del Estado de Nueva York (SUNY), tras 14 años de enseñar matematicas y estadisticas. Romeu es Fellow de la Royal Statistical Society, miembro de ASA (American Statistical Association) y IASI (Inter American Statistical Institute), Senior Member de la American Society for Quality y mantiene certificaciones de Reliability (confiabilidad) y Quality (Calidad). Dr. Romeu es autor del libro “A Practical Guide to Statistical Analysis of Material Property Data”, de estadísticas para ingenieros, y dirige el Proyecto Juárez Lincoln Marti, de Educación Internacional (http://web.cortland.edu/matresearch).

El evento califica para 0.1 RU crédito profesional si usted atiende, estos se distribuyen en su correo electrónico (e-mail) que usa para registrarse en este evento, y se manda días después del evento.

ASQ RRD Series: Analysis of Survival Data in Engineering, Business, and Medicine

Thu, Apr 8, 2021 12:00 PM – 1:00 PM EDT

Presenter: Dr. Wayne Nelson

https://register.gotowebinar.com/register/6305580850384875023

This talk is an introduction to survival data analysis in engineering, business, and medicine. It presents basic concepts including the Weibull distribution, its age dependent failure rate, and simple probability plots. The talk shows applications to engineering, business, and medicine,, including

• The reliability of products designed and manufactured by engineers (e.g., toaster life).
• The distribution of time to a bank’s loss of bank accounts and TV service’s loss of subscribers.
• The life distribution of patients under treatment and life of medical devices (e.g., heart pacemakers).
The life distribution of patients and products, e.g., median life, % surviving 5-years or warranty.
• Whether a product failure rate increases or decreases as the population ages. This information is used to determine whether preventive replacement of old units in service reduces in-service failures.
• A prediction of the number of population failures in a coming month, quarter, or year.
During product development, a prediction of the improvement in product life that would result from eliminating one or more failure modes.
• Comparisons of 1) medical treatments, 2) business policies, and 3) product designs, vendors, materials, operating environments, manufacturing methods, etc.


SPEAKER. Dr. Wayne Nelson is an expert on reliability data analysis and accelerated testing. He worked at GE Research & Development for 24 years, and now consults privately. He is a fellow of the Amer. Society for Quality, the Institute of Electrical and Electronic Engineers, and the Amer. Statistical Assoc. ASQ awarded him the Shewhart and Shainin Medals and the Hahn Award for his lifetime achievements, and the Brumbaugh and Youden Prizes for articles on innovative methodology. He was the second person to receive the Lifetime Achievement Award of the 2,000-member IEEE Reliability Society for his innovative contributions to reliability methodology and reliability education.

ASQ RRD series webinar: Robustness Thinking in Design for Reliability – A Best Practice in Design for Reliability

Thu, Mar 11, 2021 12:00 PM – 1:00 PM EST

Presenter: Matthew Hu

https://register.gotowebinar.com/register/2625796907172545805

Abstract
Reliability is one of the most important characteristics of an engineering system. Reliability can be measured as robustness over time as a leading key performance indicator (KPI). Robustness thinking is essential to improve quality and reliability proactively by factoring the activities of design for reliability. Nothing can be substituted for thinking. Early robustness development in manufacturing can reduce the variability of those processes with valuable benefits to manufacturing yields, cycle time and costs. Product Development has a huge impact on revenue stream, reliability. It is most cost-effective and less time-consuming to make design insensitive to uncontrollable user environments in upfront design phase. Robustness development in Design for Reliability (DFR) process provides benefits in reduction of early-on physical testing and traditional test-fix-test cycles. Robustness achieved early in development enables shorter cycle times in the later design phases.

Objectives of the presentation
• Define robustness
• Explain product development using Robust Engineering versus traditional product development
• Explain Robust Design for Reliability
• Define Objective Function, Basic Function, and Ideal Function
• Explain how Ideal Function and Two-step Optimization lead to robust technology development and achieve “Better, Cheaper, Faster” product development
• Explain how to conduct a preliminary robustness assessment
• Explain the value of robustness assessment
• LiDar case study in robust autonomous driving technology development

Important Takeaway
• Make design insensitive to uncontrollable user environment (Noise)
• Early development of robustness is key to proactive quality and reliability Improvement
– Capture, front load noise and manage noise
– Gain control of your product performance
– Optimize robustness – avoid all failure modes
• Apply Robust design principles at early stages of product design to “forecast” problems and take preventive action.

1.  A stand‐by redundant system uses two identical units. The failure rate of each unit is 0.0007 failures per hour. What is the system  reliability for 200 hours (Assume the sensing and switching reliability is 0.9).

A.0.991      B. 0.983      C. 0.979         D,  0.965

2. Which of the following is true if all the subsystems in a series system have a constant failure rate?

A. The failure rate of the system is constant

B. The failure rate of the system will increase as more subsystems are added

C. The failure rate of the system is the sum of the subsystem failure rates

D.  All of the above

3. What is the reliability of this system?

A. 0.9191

B. 0.9244

C. 0.9297   

D. 0.9856

4. A parallel system has three subsystems each with a reliability of R.

The system reliability can be calculated as

A. 3R

B. R3

C.  1 ‐ (1 ‐ R)3  

D.  1 ‐ (1 ‐ R3)

5. To place confidence limits on a prediction which of the following is true?

A. The Chi Square distribution is used

B. The  F distribution is used

C. The t distribution is used

D.  A prediction is probabilistic, therefore confidence does not apply

6. 50 electronic devices have been tested for 3,000 hours without failures.

What is the approximate MTBF of this   device at 90% lower confidence ?

A. 65150 hours  

B. 25500 hours 

C.  6500 hours 

D. 6500 hours

7. Which one is not the reliability prediction technique?

A. Weibull plot

B. Duane plot  

C.  Uniform Precision Design   

D. Fix effectiveness Model?

8. In success testing, how many samples need to operate for one lifetime without failure to demonstrate 95% confidence with  99% reliability?

A. 298 samples   

B.  90 samples   

C.   59 samples   

D.   458 samples

9. The following data is used for thermal stress evaluation of ICs using Arrhenius Equation.

What is the acceleration factor ?

Wearout Activation Energy is  in eV

  • Ea = 0.5 eV k is Boltzmann’s Constant,
  • 8.617 x 10-5 eV / K
  • T1 is Temperature in degrees C = 70 deg C (343⁰K)
  • T2 is Junction temperature during test in degrees C= 125 deg C  (398⁰K)

A. 10.4

B.   5.2     

C.  9.5   

D.   10.0

10. The reliability of a system consisting of two units in parallel is 0.96.

If the reliability of each component is increased by 10%, what is the percentage increase in the reliability of the system?

A. 10%

B.  5%  

C.  3.33% 

D.  2.66%

On 14th of January 2021, Bob Deysher presented: ASQ RRD Series: Auditing ISO 9001 Clause 8.3, Design and Development of Products and Services (Risk Based Thinking)

Internal auditing is a requirement in ISO 9001:2015. Its purpose is to assess if processes are maintained and effective. This webinar will review how internal auditing, as well as the use of risk based thinking (RBT), can add value to the process of design and development of product and services. Focus will be on project reviews as well as verification and validation requirements providing examples of areas that had shown prior weaknesses. It is also recognized that industry specific standards such as aerospace (AS 9100) and automotive (IATF 16949) have added additional design and development requirements. These requirements will need to be audited if the organization registered to the industry specific quality management system.

ASQ RRD Series: Weibull – Special Topics in Weibull Analysis

Feb 11, 2021 12:00 PM Eastern Time (US and Canada)

Presentor: Jim Brenneman

https://attendee.gotowebinar.com/register/2670640488601367824

Abstract:
Special Topics in Weibull Analysis: Continuation of Using Weibull Analysis to Solve REAL Engineering Problems
1. What Happens if a Weibull distribution doesn’t fit the data?   (Comparing the Weibull to other possible distributions). With some reminders/surprises.
2. Weibull Confidence Bounds and their use in Reliability. The only confidence bounds  application I have found extremely useful.
3. Regression with Life data  (Modeling S/N data, …).. Not your usual Regression analysis. You can use censored (runout) data and get results that are accurate (based on the data).
4. Sudden Death Testing. Sounds ominous, but can save you big $$ in many reliability tests.

1. Estimate the individual part failure rate given a base failure rate of  0.0333 failure/hour, a quality factor of 0.98 and an environmental stress factor of 0.92.

A. 0006

B. 0.300                      

C. 0.027                   

D. 0.0300

2. Consider a three component independent series system. The component reliabilities are described according to the following:

Component (1): Weibull (β = 1.6 η = 9,500)

Component (2): Exponential (λ = 0.000087)

Component (3): Lognormal (μ = 7.5 σ = 0.81) .

The reliability of the system at 1,000 hours is most nearly:

A. 0.6846 

B. 0.9995 

C. 0.3155  

D. 0.7673

3. A failure reporting and corrective action system should ensure that all steps are taken to :

A. Determine responsibilities for failures.

B. Record costs associated with the corrective action.

C. Identify, investigate and analyze failures.

D. Define the goals of the FRACAS team.?

4. The injection mold for a manufacturing process requires periodic maintenance due to the failure of a forming mechanism. The failures occur at a constant rate of 0.0002 per hour and the repairs occur according to a constant rate of 0.0625 per hour. The steady state probability of the system being operational is most nearly:

A. 0.0032  

B. 0.9998  

C. 0.9968  

D. 0.9375

5. Monte Carlo simulation is being used by a manufacturer to determine the probability of mechanical failure of a new design. Which of the following are required to perform this analysis?

I. Computer‐generated random numbers

II.  Stress distribution

III. Strength distribution                                        

IV.  Environmental conditions

A.   I         

B. I, II, & III     

C. All of the Above    

D. II  &  III

6. What is the best description for a Reliability Program and Reliability Information?

A. Reliability Program is more effective without Reliability Information

B. Reliability Program has nothing to do with Reliability Information.

C. Reliability Program only collects Reliability Information.

D. Reliability Program utilizes Reliability Information to improve reliability.

7. Consider the following Reliability Block Diagram:

Success will occur even if a failure occurs in the following elements?

A. I & IV  

B. I & III  

C. II & III  

D.  I & II

8. An electronic system needs any 2 out of 4 serial power supplies to be operational.

What is the reliability of this 2 out of 4 power supply design? Assuming the switching has 100% reliability.

Each power supply has the same reliability of 95%. All power supplies are activated during system operation.

A. 0.8095    

B. 0.9095    

C. 0.9595    

D. 0.9995

9. The system reliability of an active redundant or parallel system:

A. is greater than the reliability of any subsystem.

B. is equal to the reliability of the ‘best’ subsystem.

C. decreases as more redundant subsystems are added to the system.

D. increases if the subsystem with the lowest reliability is removed.

10. Given a set of test data for estimating wear‐out reliability of a mechanical part design (assuming normal distribution of the data), which of the following factors is NOT required to obtain a reliability estimate at 90% confidence level?

A. Number of Degrees of Freedom (DF)

B. Standard deviation of the population

C. Standard error of the mean

D.  Z‐score for the specified confidence level

Title: Prognostics and Health Management; Fundamentals, Elements, RUL Determination Techniques

Dec 10, 2020 12:00 PM Eastern Time (US and Canada)

Presenter: Dr. Mohammad Pourgol-Mohammad

https://attendee.gotowebinar.com/register/1030971782763021839

Abstract
The core of U.S. economy, security and quality of life depends on complex engineering systems that range from power plants, energy systems, and pipelines to aircraft, defense, and transportation systems. These complex engineering systems consistent of interconnected and diverse hardware, software, and human elements in dynamic conditions, physical processes, and
environments. Over the past decades, significant advances in sensing and computing have led to an explosion of new data and PHM algorithms designed to monitor component reliability. This webinar will discuss the advancements of PHM techniques and the simulation tools available to estimate the fatigue, corrosion, wear and creep life (RUL) of structures. Several Case studies will be presented.

Bio
Dr. Mohammad Pourgol-Mohammad is a safety/reliability analyst in multidisciplinary systems analysis with Keurig Green Mountain and Associate Professor (adj) of Mechanical engineering at University of Maryland and was an Associate Professor of Reliability Engineering, with Sahand University of Technology (SUT). He received his Ph.D in Reliability Engineering from University of Maryland (UMD), and holds one M.Sc degree in Nuclear Engineering and another in Reliability Engineering from UMD. His undergraduate degree was in Electrical
Engineering. Dr Pourgol-Mohammad has more than 18 year of work experience including research and teaching in safety applications and reliability engineering at various institutions including Johnson Controls, Sahand University of Technology, FM Global, Goodman Manufacturing, UMD, Massachusetts Institute of Technology (MIT), University of Zagreb-Croatia. He is a senior member of ASQ, ASME (currently ASME Safety Engineering and Risk/Reliability Analysis Division (SER2D) Chair), ANS and member of several technical committees and a registered Professional Engineer (PE) in Nuclear Engineering in States of Massachusetts. He is a certified reliability engineer (ASQ CRE), certified six sigma Black Belt (CSSBB) and Manager of Quality

Picture © B. Poncelet https://bennyponcelet.wordpress.com