An Introduction To Reliability And Maintainability Engineering

However, only a minority of engineers working in the discipline have this certification. The uncertainty introduced by strong model assumptions is often not quantified and presents an unavoidable risk to the system engineer. As long as the components in that path are operational, the system is operational.

An introduction to reliability and maintainability engineeringReliability Availability and Maintainability - SEBoK

Collectively, they affect economic life-cycle costs of a system and its utility. It is defined as the partial derivative of the system reliability with respect to the reliability of a component. Estimation of maintainability can be further complicated by queuing effects, resulting in times to repair that are not independent. Maintainability models present some interesting challenges. Statistical Models and Methods for Lifetime Data.

An introduction to reliability and maintainability engineering

Simple topologies include a series system, a parallel system, a k of n system, and combinations of these. Test conditions must include accurate simulation of the operating environment including workload and a means of identifying and recording failures. Win Smith is a specialized package that fits reliability models to life data and can be extended for reliability growth analysis and other analyses. There are more sophisticated probability models used for life data analysis.

An introduction to reliability and maintainability engineering

Units whose precise times of failure are unknown are referred to as censored units. System models require even more data to fit them well. Once a system is fielded, its reliability and availability should be tracked. Administrative delay such as holidays can also affect repair times. These models often have threshold parameters, which are minimum times until an event can occur.

An introduction to reliability and maintainability engineering

This requires strong assumptions be made about future life such as the absence of masked failure modes and that these assumptions increase uncertainty about predictions. The final subsection lists the more common reliability test methods that span development and operation. Second, and more importantly, reliability data is different from classic experimental data.

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. Because of its potential impact on cost and schedule, reliability testing should be coordinated with the overall system engineering effort. Methods for doing so are in the scope of software engineering but not in the scope of this section. There is also a suite of products from ReliaSoft that is useful in specialized analyses.

Often these sub-processes have a minimum time to complete that is not zero, resulting in the distribution used to model maintainability having a threshold parameter. They are usually the sum of a set of models describing different aspects of the maintenance process e.

Reliability Availability and Maintainability

An introduction to reliability and maintainability engineering

Each path through the graph represents a subset of system components. Down time might be counted only for corrective maintenance actions, or it may include both corrective and preventive maintenance actions. Models can be considered for a fixed environmental condition. Discrete distributions such as the Bernoulli, Binomial, and Poisson are used for calculating the expected number of failures or for single probabilities of success. As that characteristic degrades, we can estimate times of failure before they occur.

BlockSim models system reliability, given component data. The same continuous distributions used for reliability can also be used for maintainability although the interpretation is different i. These problems with reliability data require sophisticated strategies and processes to mitigate them. This dependency frequently makes analytical solution of problems involving maintainability intractable and promotes the use of simulation to support analysis. Evaluations based on qualitative analyses assess vulnerability to single points of failure, failure containment, recovery, temptations christmas songs and maintainability.

However, reliability and availability can also be increased through architectural redundancy, independence, and diversity. Standards are produced by both governmental agencies and professional associations, and international standards bodies such as. Of particular importance is a plan to track data on units that have not failed. Ideally, the values of the parameters used in these models would be estimated from life testing or operating experience.

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Data on a given system is assumed or collected, used to select a distribution for a model, and then used to fit the parameters of the distribution. In addition to these comprehensive tool families, there are more narrowly scoped tools. Bayesian Reliability Analysis.

Understanding user requirements involves eliciting information about functional requirements, constraints e. It is a directed, acyclic graph. The discussion in this section relies on a standard developed by a joint effort by the Electronic Industry Association and the U. Reliability, Availability, and Maintainability. This can bias an analysis.

From these emerge system requirements that should include specifications for reliability, maintainability, and availability, and each should be conditioned on the projected operating environments. Human factor analyses are necessary to ensure that operators and maintainers can interact with the system in a manner that minimizes failures and the restoration times when they do occur. Maintainability is often characterized in terms of the exponential distribution and the mean time to repair and be similarly calculated, i. Department of Defense DoD. Queue delays, in particular, are a major source of down time for a repairable system.

Some are general but more are specific to domains such as automotive, aviation, electric power distribution, nuclear energy, rail transportation, software, and many others. The goal of such testing is to determine the integrated system failure rate and assess operational suitability.