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- Mathematical Models for Systems Reliability - Benjamin Epstein, Ishay Weissman - Google книги
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These four properties concern the application of important methodologies for designing and managing complex technical systems. The present chapter analyses several reliability allocation techniques present in literature.

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Starting from well-known methodologies, two reliability allocation methods has been proposed and validated: Integrated Factors Method I. We focus on the most important conventional methods to discuss their limitations to motivate the current research. The proposed methods supply a logic for the analysis of prototype complex systems during the pre-design phase, even if it presents general characteristics that allow this logic to be extended to different design phases.

In particular, the proposed CFM method can resolve the shortcomings of the conventional methods with a new reliability approach useful to series-parallel configurations in order to obtain important cost savings. In fact, the results show that the most conventional reliability allocation methods have one fundamental problem: in general, they are designed for complex system with series-configurations preliminary phase design but not for series-parallel configurations. The result is an increase of reliability allocated to units series configuration in order to guarantee the reliability target system extremely low failure rate.

Please log in to get access to this content Log in Register for free. To get access to this content you need the following product:. Springer Professional "Technik" Online-Abonnement. As the trend during system development is the growing of system reliability, reliability growth models, each of them is tend to represent the growing trend, are acting as a guide help with measure and achieve this reliability growth resulting from improved software reliability and recovery algorithms.

Many mathematical models exist nowadays, their basic working principle is to apply the testing results, data points, to the model, and based on the degree of matching or deviation from the model, judgment is made as of whether or not the system's development is coming towards reliability growth, and how much amount of refinement work need to be done right to this point of time.

The key that matters here with reliability growth model is how well each model represent the real system development process so that the refinement work done corresponding could really effectively and efficiently improve system reliability growth.

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As with any physical process, randomness is to be expected. So our understanding from this is that the key issue for reliability growth model is the degree of its representing the system reliability growing trend. Now the fact is that more and more mathematical models come out, and tend to make those models more and more complex, which raises the question, is this diversity necessary. With the discussion going on, people show the preference of choosing simpler models.

Reliability Growth and Reliability Prediction [2]. As the concept of reliability growth has the in-born character of simplicity and easy understandability, it gets real popularly used. But this popularity also brings up the problem of improper applying of it in not a few cases and conditions, which make some of the researchers feel urgent to give it a clarification as of the use of this concept. Reasonable application of reliability learning curve: Determine approximate reliability test time requirements; monitor rate of reliability improvement in test.

## Mathematical Models for Systems Reliability - Benjamin Epstein, Ishay Weissman - Google книги

Unreasonable use of this curve: Predicting equipment reliability, either current or future; used to combine different types of reliability tests. The process of reliability growth has one primary objective - to improve the reliability of the design through analysis and test. Resources are always limited, so the reliability growth process must be as efficient as possible. A collateral objective of reliability testing, indeed, of all development testing, is to validate the models and tools used in creating the design.

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The underlying technology is an obvious limiting factor in the degree of improvement possible in a design. Rather than relying on a continuous series of technological breakthroughs, design engineers must focus on the fundamentals and thoroughly understand the technologies at hand. And the understanding of the design team is another constraint on the degree of improvement possible in the reliability, or any other performance characteristic, of a product. The models and tools used in creating a design reflect the current level of understanding of the technical community.

To some extent, the models and tools are always inexact.

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By using test results to validate the models and tools and to revise or update them when we find they are not valid, our knowledge increases and the potential improvements possible for design increase. Although improvement of a design's reliability is the primary objective of the reliability growth process, it is also an important means for improving our models and tools used in creating a design.

Reliability growth testing, one aspect of the reliability growth process, is also being used to assess the level of product reliability being achieved.