Quanterion announces the publication of a valuable guidance in the application of reliability growth concepts in improving products and systems. “Achieving System Reliability Growth Through Robust Design and Test” offers new definitions of how failures can be characterized, and how those new definitions can be used to develop metrics that will quantify how effective a Design for Reliability (DFR) process is in (1) identifying failure modes and (2) mitigating their root failure causes. Historically, the reliability growth process has been thought of, and treated as, a reactive approach to growing reliability based on failures “discovered” during testing or, most unfortunately, once a system/product has been delivered to a customer. This book explores the pre-test activities and opportunities that can be leveraged to promote and achieve reliability growth during the design phase of the overall system life cycle. The ability to do so as part of an integrated, proactive design environment has significant implications for developing and delivering reliable items quickly, on time and within budget. For more information or to purchase the book, Click here..
- 2nd Annual Cybersecurity Awareness Colloquia
- Quanterion Solutions Exhibits at AFWERX Fusion
- Quanterion Solutions is Hosting a Webinar: “COVID-19 Lessons Learned from the Epicenter”
- Registration Available for Open Training – Winter 2020 Session!
- Registration Available for: Open Training – Fall 2020 Session!
- Quanterion Turns 20!
Recent Reliability Ques
- Confidence Bounds on the Mean Time Between Failure (MTBF) for a Time-Truncated Test
- Maintenance Planning with Wearout Failure Modes
- Maintenance Planning with a Constant Failure Rate
- Maintenance Planning with Early Failures
- Interference Stress/Strength Analysis
- Models Commonly Used to Measure Reliability Growth
- How Good Is Your Reliability Approach?
- Solving the Complex RBD-Series Parallel System with a Keystone Component
- Environmental Stress Screening: Basic Steps in Choosing an ESS Profile
- Design of Experiments for Reliability Improvement
- Test Samples: How Many Are Needed?