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Practical Reliability of Electronic Equipment and Products by Eugene R. Hnatek

Please enter a valid ZIP Code. No additional import charges at delivery! This item will be shipped through the Global Shipping Program and includes international tracking. This process differs significantly from the one usually taught in an introductory statistics course. First, the normal distribution is seldom used as a life distribution, since it is defined for all negative times. Second, and more importantly, reliability data is different from classic experimental data.

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Reliability data is often censored, biased, observational, and missing information about covariates such as environmental conditions. Data from testing is often expensive, resulting in small sample sizes. These problems with reliability data require sophisticated strategies and processes to mitigate them. In most large programs, RAM experts report to the system engineering organization. At project or product conception, top level goals are defined for RAM based on operational needs, lifecycle cost projections, and warranty cost estimates.


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These lead to RAM derived requirements and allocations that are approved and managed by the system engineering requirements management function. RAM testing is coordinated with other product or system testing through the testing organization, and test failures are evaluated by the RAM function through joint meetings such as a Failure Review Board. In some cases, the RAM function may recommend design or development process changes as a result of evaluation of test results or software discrepancy reports, and these proposals must be adjudicated by the system engineering organization, or in some cases, the acquiring customer if cost increases are involved.

Once a system is fielded, its reliability and availability should be tracked.


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  6. Reliability and Failure of Electronic Materials and Devices.
  7. Such a system captures data on failures and improvements to correct failures. This database is separate from a warranty data base, which is typically run by the financial function of an organization and tracks costs only.

    Failure Analysis: A Practical Guide for Manufacturers of Electronic Components and Systems

    Unfortunately, the lack of careful consideration of the backward flow from decision to analysis to model to required data too often leads to inadequate data collection systems and missing essential information. Proper prior planning prevents this poor performance. Of particular importance is a plan to track data on units that have not failed.

    Units whose precise times of failure are unknown are referred to as censored units. Inexperienced analysts frequently do not know how to analyze censored data, and they omit the censored units as a result. This can bias an analysis.

    Because of the importance of reliability, availability, and maintainability, as well as related attributes, there are hundreds of standards associated. Some are general but more are specific to domains such as automotive, aviation, electric power distribution, nuclear energy, rail transportation, software, and many others. Standards are produced by both governmental agencies and professional associations, and international standards bodies such as. The following table lists selected standards from each of these agencies.

    Reliability of Electronic Components A Practical Guide to Electronic Systems Manufacturing

    Because of differences in domains and because many standards handle the same topic in slightly different ways, selection of the appropriate requires consideration of previous practices often documented as contractual requirements , domain specific considerations, certification agency requirements, end user requirements if different from the acquisition or producing organization , and product or system characteristics. Becoming a reliability engineer requires education in probability and statistics as well as the specific engineering domain of the product or system under development or in operation.

    A number of universities throughout the world have departments of reliability engineering which also address maintainability and availability and more have research groups and courses in reliability and safety — often within the context of another discipline such as computer science, system engineering, civil engineering, mechanical engineering, or bioengineering.

    Because most academic engineering programs do not have a full reliability department, most engineers working in reliability have been educated in other disciplines and acquire the additional skills through additional coursework or by working with other qualified engineers. However, only a minority of engineers working in the discipline have this certification. Reliability can be characterized in terms of the parameters, mean, or any percentile of a reliability distribution.

    However, in most cases, the exponential distribution is used, and a single value, the mean time to failure MTTF for non-restorable systems, or mean time between failures MTBF for restorable systems are used. The metric is defined as. Maintainability is often characterized in terms of the exponential distribution and the mean time to repair and be similarly calculated, i. Where is the total down time and noutages is the number of outages. As was noted above, accounting for downtime requires definitions and specificity. Down time might be counted only for corrective maintenance actions, or it may include both corrective and preventive maintenance actions.

    Where the lognormal rather than the exponential distribution is used, a mean down time can still be calculated, but both the log of the downtimes and the variance must be known in order to fully characterize maintainability. As was the case with maintainability, availability may be qualified as to whether it includes only unplanned failures and repairs inherent availability or downtime due to all causes including administrative delays, staffing outages, or spares inventory deficiencies operational availability.

    Probabilistic metrics describe system performance for RAM.

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    Quantiles, means, and modes of the distributions used to model RAM are also useful.