RMMR 2019: ASQ Reliability and Risk Division 50th anniversary Conference

ASQ Reliability and Risk Division is proud to present the “RMMR” conference.
Celebrating the 50th Anniversary of the ASQ Reliability and Risk Division.
The “Reliability, Maintenance & Managing Risk” Conference would be the first installment of a new conference for the Reliability and Risk practitioners to connect and learn .
The location will be San Antonio, TX.
The conference dates are: Oct 15-17
(with pre/post conference courses on: Oct 14 & 18)
More info will follow soon!
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Slides and recording – ASQ RRD Series: Dispelling the myth that Quality and Reliability are ‘”Kissing Cousin’s”

On Thu, Dec 13, 2018 Adam Bahret presented “Dispelling the myth that Quality and Reliability are ‘”Kissing Cousin’s””

Reliability and Quality are often referred to as “Kissing Cousin’s” The implication is that Reliability is a derivative of Quality. This couldn’t be farther from the truth. Quality is focused on screening and improving production of a finalized design. Reliability is focused on creating a reliable design. There is little overlap between those two objectives, different tools, different program activities, different participants. In this webinar Adam Bahret of Apex Ridge will explore the difference between Quality and Reliability and highlight how Reliability is a fundamental component of the design process and the design itself. 

Below a link with the slides of this webinar.

ASQ RRD – Dispelling the myth that Quality and Reliability are Kissing Cousins

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Slides and recording – ASQ RRD Series: Big Data Analytics – Telematics Data Analysis

On Thu, Nov 8, 2018  Dennis Craggs presented “ASQ RRD Series: Big Data Analytics – Telematics Data Analysis”

Engineers conduct tests to verify that product meet engineering standards. These standards were developed with customer surveys, duplication past standards, or relying on expert opinions. Modern technology provides a new tool to validate standards by measuring product usage by the customer. An automotive example will be discussed, but the methods have much broader application.
Automobile companies installed telematics modules on fleet vehicles, with the consent of the owner, to collect and store usage and environmental data. The volume of data was enormous. Different analyic methods were required for different data types but there were only a few data types to consider. The raw data needed to be standardized for different vehicle miles or time. Then counting data, like trips/day, were analyzed with simple probability distributions. State data, like switch setting, need to be analyzed for transitions and time duration in states. Continuous measurements are more difficult. Engineers frequently use bar histograms, but bar histograms quickly become unreadable when two or more data sets were combined in the same graphic. Methods were developed that allowed many histograms to be analyzed fleet usage patterns and develop the 5th, 50th, and 95th customer percentiles. The standards can then be validated using the customer data. 

Below a link with the slides of this webinar.

ASQ RRD – Telematics Data Analysis

 

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SLIDES AND RECORDING – ASQ RD Webinar Series – Catalysts for Human Error in Complex System Design_Glitches and Representations in Thought and Action

On Thu, Oct 11, 2018 Audrey Murphy presented “Catalysts for Human Error in Complex System Design:Glitches and Representations in Thought and Action”

Statistics suggests that over 70% of human error is associated with system design failure where catastrophic incidents are reported. A renewed awareness for the complexity of human thought processes could contribute to insights into system design failure specific to human errors. Characterized as underlying structures in the mind, glitches and representations in thought and action are important aspects of human error research because both attributes represent fuzzy and incomplete thought processes. Glitches in thought refer to acceptance of systematic patterns of one’s preferences and beliefs as opposed to consideration of contradicting information. Representations in thought and action refer to external reality that is incomplete and inconsistent in the mind that underpin preferences, actions, and behaviors, which helps to inform and facilitate ways that human beings act on their environment. These intimate relationships and connections of thought processes also involve powerful social forces responsible for the primary reasons of all human action. Grounded in a scholar-practitioner model, emphasis in this presentation is concerned with the recursive nature of interaction and transaction of goals and purpose of human thought and action relative to system design failure. This presentation also integrates research findings and direct quotes from my recently published dissertation, which is a qualitative multiple case study situated at the intersection of cognitive psychology and social systems. In addition, practical examples of why and how human beings are susceptible to errors is discussed. Possible ways to prevent the likelihood of human error with reference to future challenges of growing system complexity are also considered.

Below a link with the slides of this webinar.

ASQ-RRD – Catalysts for Human Error in Complex System Design

 

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SLIDES AND RECORDING – ASQ RD Webinar Series – Risk Based Decision Making

Thu, Sep 13, 2018 Jim Breneman presented “Risk Based Decision Making”

As most of you are well aware, ISO 9001:2015 is a risk-based standard. In addition to the Quality Systems Management that must recognize risk and opportunities in all aspects of a business (Sections 4.1 & 4.2 of ISO9001-2015); Section 6 states that the organization shall “determine risks and opportunities that need to be addressed.” Thus we have arrived at the need for Risk-Based thinking and Risk Management.
But since there are typically too many risks, and not enough money to address all of them, how and what do we do? First you have to set a Risk “goal” (in terms of Reliability & –possibly Safety—depending on the product). Allocate this top-level Risk goal among the sub-systems (and lower if that makes sense). This will set the Design Reliability (& Safety) goals.
This presentation will illustrate some examples of how to model reliability via Weibull Analysis, Reliability Growth Modeling, Fault Tree Analysis, FMEA and Monte Carlo Analysis to project Risk (and achieve a reliable and safe product).
Of the Risk Management processes this presentation will concentrate in the areas of
• Qualitative risk analysis
• Quantitative risk analysis
Examples using various Reliability & Statistical tools (FMEA, Weibull Analysis, Monte-Carlo Simulation, and others) will illustrate “calculating” risk and how to prioritize risks against a “standard.”… even when your data is sparse (or possibly non-existent).
In addition, you’ll see some methods to help in telling the Boss bad news: “We can’t do this project in the time frame (a budget – time risk to the company) as quickly as you want.” (without getting thrown out of his office, or worse). 

Below a link with the slides of this webinar.

ASQ RD Webinar Series – Risk Based Decision Making

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Webinar RECORDING – ASQ RRD SERIES: Human-in-the-Loop: Predictive Modeling of the Likelihood of a Vehicular Mission

On Thu, May 10, 2018 Ephraim Suhir presented “Human-in-the-Loop: Predictive Modeling of the Likelihood of a Vehicular Mission”

Improvements in successful and safe operation of an aerospace, maritime, or an automotive vehicle can be achieved through better ergonomics, better work environment, and other efforts of the traditional human psychology and ergonomic science that directly affect human behaviors and performance. There is also a significant potential for further reduction in vehicular accidents and casualties and for assuring the success and safety of a vehicular mission or an extraordinary situation through better understanding the role that various uncertainties play in the planner’s and operator’s worlds of work, when never-perfect human, never completely failure-free navigation equipment and instrumentation, never 100%-predictable response of the object of control (an air- or a space-craft, a boat, or a car), and uncertain and often harsh environments contribute jointly to the likelihood of a never-completely-failure-free mission or a situation. By employing quantifiable and measurable ways of assessing the role and significance of various critical uncertainties and treating a human-in-the-loop as a part (often the most critical part) of a complex man–instrumentation–equipment–vehicle–environment system, one could improve dramatically the state-of-the-art in assuring, on the probabilistic basis, a vehicular mission success and operational safety. The webinar addresses mostly aerospace missions and off-normal situations, but indicates also how the suggested models and methodologies can be applied in automotive and maritime engineering. The general concepts are illustrated by practical examples. 

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SLIDES AND RECORDING – ASQ RD Webinar Series – Essential Competencies for Improving Software Development

On Thu, Jul 12, 2018 Linda Westfall presented “Essential Competencies for Improving Software Development”

Traditionally, manufacturing based quality approaches have focused on our ability to replicate multiple copies of a product over and over again to an exact specification within acceptable variation. While the replication process does exist for software (for example, making many copies of a CD), it is rarely the cause of major software quality and reliability issues. Another reliability issue is that physical equipment and products “wear out” over time so preventive maintenance and calibration are important considerations. However, software is not a physical entity and therefore it does not “wear out”, once a defect is removed from the software, it is gone for good as long as good configuration management practices are in place.

Since software defects rarely come from replication and they do not occur because of physical deterioration over time — software is different. Software quality are concerned with the requirements, design, implementation, verification and validation processes used to develop the software. This webinar will explore some of the essential competencies involved in shifting traditional quality approaches to a software development focus.

Below a link with the slides of this webinar.

ASQ RRD – Essential Competencies for Improving Software Development

 

 

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TECH SPOT: SAMPLE CRE QUESTIONS (Part 4)

Answers to CRE questions in December 2018 Newsletter

The mean number of life units during which all parts of the item perform within their specified limits, during a particular measurement interval under stated conditions’. This is the definition of ___________ for a repairable item.
o MTBF ‐ Mean Time Between Failures                             
o MTBM ‐ Mean Time Between Maintenance
o MTTF ‐ Mean Time To Failure                                          
o MDT ‐ Mean Maintenance Downtime

The sum of corrective maintenance times at any specific level of repair, divided by the total number of failures within an item repaired at that level, during a particular interval under stated conditions? This is the definition of __________.
o MTBM ‐ Mean Time Between Maintenance                 
o MTBF ‐ Mean Time Between Failures
o MTTR ‐ Mean Time To Repair                                           
o MDT ‐ Mean Maintenance Downtime

When a test is terminated before all units fail the resulting data are known as:
o right censored                                                                     
o interval data
o left censored                                                                       
o None of these

The main responsibilities of a Failure Review Board include all except which of the following?
o Assessing failures                                                                                        
o Setting up accelerated life tests
o Monitoring Corrective Action to prevent failure Reoccurrence           
o Monitoring reliability growth.

Which one is the most important input for Reliability Program development?
o MTBF prediction data                                  
o Customer requirements from their mathematical modeling  data
o Accelerated life test data                            
o Customer requirements from their empirical data

Customer inputs at the concept stage of design are used to drive all except which of the following?
o Initial reliability predictions based on field data              
o Development of operational profiles
o Criteria for acceptable product performance                   
o Identification of suitable design verification tests

In which of the following life cycle phases is Reliability prediction most useful as an aid during tradeoffs between various operating scenarios (such as operating conditions, customer usage)?
o Concept                                  
o Production
o Development                         
o Test

Manufacturing errors are the primary reason for products failing during the:
o infant mortality stage.                                     
o wear‐out stage.
o useful life stage.                                               
o infant mortality and wear‐out stages

During its useful life period, the MTTF for a particular type of light bulb is 2000 hours. Out of 1000 of these bulbs, how many will have burned out after 1500 hours?
o 472                                          
o 736
o 528                                          
o 750

A unit has a design life of 1,000 days. It has a MTBF that is estimated to be 50 days, requiring a mean time to repair of 15 days. The acquisition cost for this unit is $150,000. Each time that the unit fails, a fixed cost of $3,000 is incurred  along with a variable cost of $500/day. What is the expected cost of this unit over its design life?
o $240,000                               
o $360,000
o $150,000                             
o $210,000

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

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ASQ RRD SERIES: Testing Techniques – Making Evidence Based Decisions

Thu, Feb 14  12:00 PM – 1:00 PM EST

by Professional Analysis and Consulting, Inc.: Timothy M. Hicks, PE and Roch J. Shipley, PhD, PE, FASM

           

https://attendee.gotowebinar.com/register/136745556869455363

 

Testing is a very broad topic. Various types of tests are performed throughout the life cycle of a product or component. These tests range from:

  • Pre-production testing
  • Audit testing to verify production intent
  • Reliability testing once a product is in production

The focus of this presentation will be on reliability testing and to provide an overview of the types of tests that are available, what specific tests are utilized for, and provide examples of successfully implemented tests.

When a product does not perform as expected, the process of finding the reason why is commonly referred to as failure analysis. Materials and components do not really “fail”; they may fracture when overloaded or when corrosion is involved due to an aggressive environment. These failures can be attributed to either design related issues or inappropriate application or use. Therefore, the only “failure” is a failure to meet expectations. Materials characterization and testing is a critical element of the failure analysis process. Categories of the materials characterization testing techniques that will be discussed include:

  • Plastics / Polymer Analysis
  • Metals Analysis
  • Coatings / Surface Analysis
  • Corrosion Analysis

BIOS:
Timothy M. Hicks, PE
Mechanical Engineer
BS – Michigan Technological University
MS – Rensselaer Polytechnic Institute
Industry – 35 years experience
27 years in design, testing, and manufacturing
8 years in engineering consulting

Roch J. Shipley, PhD, PE, FASM
Materials Engineer
BS and PhD – Illinois Institute of Technology
Industry – 38 years experience
10 years in manufacturing and corporate research
28 years in engineering consulting

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ASQ RRD Series: An Introduction to Uncertainty Quantification for Reliability & Risk Assessments

Thu, Jan 10, 2019 12:00 PM – 1:00 PM EST

by Mark Andrews, Ph.D. 

RSVP:

https://attendee.gotowebinar.com/register/9156327511401342721

Numerical simulations have become the choice approach for performing analytics in many industrial sectors. With the phenomenal growth in computational power and significant advancements made in Computer-Aided Engineering (CAE) software, computer experiments of complex systems are now capable of reducing the dependency and costs of conducting physical experiments. While the prevalence of simulation tools offers unique potential to generate expedient analytics, simulation modeling of complex systems requires Uncertainty Quantification, an advanced analytical methodology capable of generating actionable results.

 Uncertainty Quantification is a multi-disciplinary field that brings together statistics, applied mathematics, and computer science to quantify uncertainties in numerical simulations. Like Six Sigma, Uncertainty Quantification makes use of statistical models to find feasible solutions to problems involving variability. However, the two methodologies seek to meet different objectives.

 This webinar will begin by introducing the topic of Uncertainty Quantification along with the basic methods and processes used to quantify uncertainties. Illustrative examples will be used to highlight how UQ can enhance Six Sigma.

Presenter:
Mark Andrews, Ph.D. 
Technology Steward
SmartUQ

Dr. Mark Andrews, UQ Technology Steward, is responsible for advising SmartUQ on the industry’s UQ needs and challenges and is the principal investigator for SmartUQ’s project with Probabilistic Analysis Consortium for Engines (PACE) developed and managed by Ohio Aerospace Institute (OAI). He recently received the award for best training at 2018 the Conference on Advancing Analysis & Simulation in Engineering (CAASE). Before SmartUQ, Dr. Andrews spent 15 years at Caterpillar where he worked as Senior Research Engineer, Engineering Specialist in Corporate Reliability, and Senior Engineering Specialist in Virtual Product Development. He has a Ph.D. in Mechanical Engineering from the New Mexico State University.

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