Fallacies of Statistical Significance

Statistical significance (or hypothesis) tests, and the related concept of p-values, are popular tools in statistical data analysis. Unfortunately, the practical implications of statistical significance often turn out to be limited and are frequently misinterpreted and overstated, or even stated incorrectly

Improving Reliability Through Warranty Data Analysis

Today\u27s emphasis on proactive improvement calls for building high reliability into products at design. The goal is to avoid field failures during a product\u27s estimated lifetime. But despite best efforts, field failures, especially on newly released products, sometimes still happen. So you need to establish processes that address such failures, mitigate their impact and, most importantly, prevent their repetition. Warranty data are frequently used for this purpose. Establishing a process that ensures up front the needed data are gathered is the most important -- and sometimes the most neglected -- part of most reliability analyses. The major usefulness of the reliability tracking system is its dynamic nature. Its key benefit is not the retrospective evaluation after four years but the information it provides much sooner. The system helped those responsible detect, pinpoint and remove problems appreciably sooner than would have been possible without the system

Planning Life Tests For Reliability Demonstration

Engineers and managers need information about reliability before making important product design and product release decisions. A reliability demonstration test shows-with a specified level of statistical confidence-the reliability meets a target value. It uses test results to determine a lower statistical confidence bound on reliability. If this bound equals or exceeds the target, demonstration is achieved. Demonstrating high reliability for a complicated system is difficult with tests of reasonable size and length. System reliability models are, therefore, used to assess reliability. The reliabilities of the system\u27s life limiting components provide important inputs to such models. This article focuses on zero failure tests - that is, reliability demonstration is achieved only if no units fail

Identify and Act: Performing product life data analysis with unidentified subpopulations

The National Highway Traffic Safety Administration ( HTSA) in 2015 recalled 28 million vehicles equipped with Takata air bag due to inflators rupturing during deployment.\u27 It was determined that moisture could penetrate the inflator canister and make the propellant more explosive over time

Predicting Problems

Manufacturers must frequently predict the number of future field failures for a product using past field-failure data, especially when an unanticipated failure mode is discovered in the field. Such predictions are needed to quantify future warranty costs and ensure a sufficient number of spare parts will be available to quickly repair failed units. In extreme cases, failure predictions are also needed to decide whether a recall is warranted and, if so, which segments of the product population must be recalled -- such as the units built during a specified period of time or those produced in a particular plant. Using an example of a fictitious company dealing with a failed part, this article will describe statistical methods for making these predictions

Improve Your Evaluations: Bayesian Methods Use Prior Knowledge in Life Analyses

In an earlier statistics roundtable column, the authors described how the conclusions you can draw from statistical analysis of limited life data can be bolstered by appropriately incorporating engineering knowledge and experience into the analysis. Now, let them demonstrate how Bayesian methods can be used as an alternative in these evaluations. The preceding analyses provided useful insights. Management, however, wanted a more definitive analysis with a single quantitative estimate of reliability and the associated statistical uncertainty. There has been a substantial increase hi the use of Bayesian methods during the past 20 years. Today, most of these applications use Monte Carlo simulations to generate a sample from the desired joint posterior distribution. Traditional methods require various assumptions -- for example, a Weibull distribution for time to failure and representative samples and test environments -- that demand careful examination. Bayesian methods require the further assumption era prior distribution based on existing knowledge

Reliability disasters: technical learnings from past mistakes to mitigate and avoid future catastrophes

When products fail in the field, disasters can result. To head off problems, manufacturers must build reliability into the design of products and processes. Statistics can be used proactively to help improve reliability during product design and development and enable manufacturers to “do it right the first time.” The authors describe some technical and statistical problems from four specific reliability disasters to highlight lessons learned

Reliability Assessment By Use-Rate Acceleration

Statistical evidence is often needed to show that a proposed product meets or exceeds its reliability goals. Many times, such evidence must be obtained in a compressed time period. Accelerated use-rate testing might be appropriate in testing other products such as photocopiers, printers, bicycles and laptop computers. A new model motor had been built for use in washing machines. Skilled design engineers used top quality materials and state-of-the-art methods to correct reliability problems on previous designs. They also performed short highly accelerated life tests, subjecting components and a few prototype motors to intensive temperature cycling, vibration and overvoltage conditions to discover, understand and remove potential failure modes. Physical evaluation indicated that a manufacturing defect was the root cause of its four failures. All failed motors, plus a sample of the unfailed ones, were taken apart and evaluated to obtain information to improve future product reliability

Timely Reliability Assessment: Using destructive degradation tests

Often, you must demonstrate long-term product or component reliability within a relatively short time window. For example, it is not uncommon to need to assess 10-year reliability from a six-month duration test program. In such situations, basing life data analyses on appropriately selected degradation data (that is, monitoring change of one or more relevant quality or performance characteristics) when coupled with test acceleration can be highly useful. This column will consider the frequently encountered case in which the degradation measurement is destructive and, consequently, only a single degradation measurement can be obtained on each test unit. In such cases, the reliability assessment may be based on an accelerated destructive degradation test (ADDT). The authors explain ADDTs using an example dealing with a seal to be used in a new product. The results are clearly comforting to the investigators because they demonstrated statistically the desired level of reliability

How To Analyze Reliability Data For Repairable Products

Leveraging powerful -- yet simple methods of reliability data analysis of repairable products or systems can help you stay on the right track. Typically, data on repairable products are obtained through field, repair or warranty information. Repairable products -- unlike nonrepairable ones -- can lead to multiple event times on the same unit, or recurring events, resulting in recurrence data. Recurrence data require special methods of analysis. Many questions about the reliability of a repairable product, based upon field repair recurrence data, can be answered by estimating its mean cumulative function (MCF). The MCF of a product at age t is defined as the average number of failures per unit up to time t. In estimating the MCF, the exposure time could vary appreciably from unit to unit within orders. This may be because of staggered entry of units into the field or differences in use rates