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

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 (

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

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

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.

ASQ RRD Series: Weibull – Special Topics in Weibull Analysis

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

Presentor: Jim Brenneman

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.