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9. What kind of aspects were lightened there positive or negative? Or may be neutral?

10.What kinds of graphics were used in this text?

Discussing

и бRELIABILITY SEQUENTIAL TEST PLAN

7. What must be done with the results of tests?

8. Compare your Plan with the plan given below. What is common and

distinguishing in it?

The purpose of reliability testing is to discover potential problems with the design as early as possible and, ultimately, provide confidence that the system meets its reliability requirements.

levels. (The test level nomenclature varies among applications.) For example,

Reliability testing may be performed at several levels. Complex systems may be tested at component,Аcircuit board, unit, assembly, subsystem and system

performing environmental stress screening tests at lower levels, such as piece parts

or small assemblies, catches problems before they cause failures at higher levels.

System reliability is calculated at each test level. Reliability growth techniques and failure reporting, analysis and corrective active systems (FRACAS) are often employed to improve reliability as testing progresses. The drawbacks to such extensive tests are time and expense. Customers may choose to accept more risk by eliminating some or all lower levels of testing.

Testing proceeds during each level of integration through full–up system testing, developmental testing, and operationalДtesting, thereby reducing program risk.

It is not always feasible to test all system requirements. Some systems are

prohibitively expensive to test; some failure modes may take years to observe; some complex interactions result in a huge number of possible test cases; and some tests require the use of limited test ranges or other resources. In such cases,

The desired level of statistical confidence also plays an important role in reliability testing. Statistical confidence is increased by increasing either the test time or the number of items tested. Reliability test plans are designed to achieve the specified reliability at the specified confidence level with the minimum number of test units and test time. Different test plans result in different levels of risk to the producer and consumer. The desired reliability, statistical confidence, and risk levels for each side influence the ultimate test plan. Good test requirements ensure

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that the customer and developer agree in advance on how reliability requirements will be tested.

A key aspect of reliability testing is to define «failure». Although this may seem obvious, there are many situations where it is not clear whether a failure is really the fault of the system. Variations in test conditions, operator differences, weather, and unexpected situations create differences between the customer and the system developer. One strategy to address this issue is to use a scoring conference process. A scoring conference includes representatives from the customer, the developer, the test organization, the reliability organization, and sometimes independent observers. The scoring conference process is defined in the statement of work. Each test case is considered by the group and «scored» as a success or failure. This scoring is the official result used by the reliability engineer.

СAs part of the requirements phase, the reliability engineer develops a test strategy with the customer. The test strategy makes trade–offs between the needs

и proceduresбare developed for each reliability test, and results are documented in

of the reliability organization, which wants as much data as possible, and

constraints such as cost, schedule, and available resources. Test plans and

official reports.

9. Study the example of the plan of Accelerated testing and give your own plan of the test of any other car system.

representative, environment.АIn such a test the product is expected to fail in the lab just as it would have failed in the field, but in much less time. The main objective of an accelerated test is either of the following:

ACCELERATED TESTING

The purpose of accelerated life testing is to induce field failure in the

laboratory at a much faster rate by providing a harsher, but nonetheless

• to discover failure modes;

• to predict the normal field life from the high stress lab life.

10. Discuss Reliability requirements for an automobile.

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11. Read and discuss information about System of reliability parameters.

SYSTEM RELIABILITY PARAMETERS

Requirements are specified using reliability parameters. The most common reliability parameter is the Mean Time Between Failures (MTBF), which can also Сbe specified as the failure rate or the number of failures during a given period. These parameters are very useful for systems that are operated on a regular basis, such as most vehicles, machinery, and electronic equipment. Reliability increases as the MTBF increases. The MTBF is usually specified in hours, but can also be

used with other units of measurement such as miles or cycles. In other cases, reliability is specified as the probability of mission success. For example, reliability of a scheduled aircraft flight can be specified as a dimensionless probability or a percentage referred to system safety engineering.

иExamples include automobile airbags, thermal batteries and missiles. Single–shot reliability is specified as a probability of success, or is subsumed into a related parameter. Single–shot missile reliability may be incorporated into a requirement for the probability of hit.

A special case of mission success is the single–shot device or system. These are devicesбor systems that remain relatively dormant and only operate once.

For such systems,Аthe probability of failure on demand (PFD) is the

reliability measure. This PFD is derived from failure rate and mission time for non–repairable systems. For repairable systems, it is obtained from failure rate and mean–time–to–repair (MTTR) and test interval. This measure may not be unique for a given system as this measure depends on the kind of demand. In addition to system level requirements, reliability requirements may be specified for critical subsystems. In all cases, reliability parameters are specified with appropriate statistical confidence intervals.

•Reliability modeling.

•Reliability test requirement.

•Design for reliability.

RELIABILITY MODELINGИ

Reliability modeling is the process of predicting or understanding the reliability of a component or system. Two separate fields of investigation are common: The physics of failure approach uses an understanding of the failure mechanisms involved, such as crack propagation or chemical corrosion; The parts stress modeling approach is an empirical method for prediction based on counting the number and type of components of the system, and the stress they undergo during operation.

For systems with a clearly defined failure time (which is sometimes not given for systems with a drifting parameter), the empirical distribution function of

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these failure times can be determined. This is done in general in an accelerated experiment with increased stress. These experiments can be divided into two main categories:

Early failure rate studies determine the distribution with a decreasing failure rate over the first part of the bathtub curve. Here in general only moderate stress is necessary. The stress is applied for a limited period of time in what is called a censored test. Therefore, only the part of the distribution with early failures can be

Сdetermined.

In so–called zero defect experiments, only limited information about the failure distribution is acquired. Here the stress, stress time, or the sample size is so low that not a single failure occurs. Due to the insufficient sample size, only an

иThe empirical failure distribution is often parameterized with a Weibull or a log– normal model.

upper limit of the early failure rate can be determined. At any rate, it looks good for the customer if there are no failures.

In a study of the intrinsic failure distribution, which is often a material property, higher stresses are necessary to get failure in a reasonable period of time.

Several degreesбof stress have to be applied to determine an acceleration model.

It is a general praxis to model the early failure rate with an exponential

distribution. This less complex model for the failure distribution has only one

(e.g. the shape parameterАof a Weibull distribution, or its confidence interval (e.g. by an MLE / Maximum likelihood approach) – and the sample size is much smaller.

parameter: the constant failure rate. In such cases, the Chi–square distribution can

be used to find the goodness of fit for the estimated failure rate. Compared to a

model with a decreasing failure rate, this is quite pessimistic. Combined with a

zero–defect experiment this becomes even more pessimistic. The effort is greatly

reduced in this case: one does not have to determine a second model parameter Д

RELIABILITY TEST REQUIREMENTS

Because reliability is a probability, even highlyИreliable systems have some chance of failure. However, testing reliability requirements is problematic for several reasons. A single test is insufficient to generate enough statistical data. Multiple tests or long–duration tests are usually very expensive. Some tests are simply impractical. Reliability engineering is used to design a realistic and affordable test program that provides enough evidence that the system meets its requirement. Statistical confidence levels are used to address some of these concerns. A certain parameter is expressed along with a corresponding confidence level: for example, an MTBF of 1000 hours at 90% confidence level. From this specification, the reliability engineer can design a test with explicit criteria for the number of hours and number of failures until the requirement is met or failed.

The combination of reliability parameter value and confidence level greatly affects the development cost and the risk to both the customer and producer. Care is needed to select the best combination of requirements. Reliability testing may be performed at various levels, such as component, subsystem, and system. Also,

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many factors must be addressed during testing, such as extreme temperature and humidity, shock, vibration, and heat. Reliability engineering determines an effective test strategy so that all parts are exercised in relevant environments. For systems that must last many years, reliability engineering may be used to design an accelerated life test.

Reliability engineering must also address requirements for various reliability tasks and documentation during system development, test, production, and Сoperation. These requirements are generally specified in the contract statement of work and depend on how much leeway the customer wishes to provide to the contractor. Reliability tasks include various analyses, planning, and failure reporting. Task selection depends on the criticality of the system as well as cost. A иcritical system may require a formal failure reporting and review process throughout development, whereas a non–critical system may rely on final test reports. The most common reliability program tasks are documented in reliability program standards, such as MIL–STD–785 and IEEE 1332. Failure reporting analysis andбcorrective action systems are a common approach for product/process

reliability monitoring.

DESIGN FOR RELIABILITY

Design for Reliability (DFR), is an emerging discipline that refers to the process of designing reliability into products. This process encompasses several tools and practices andАdescribes the order of their deployment that an organization needs to have in place in order to drive reliability into their products. Typically, the first step in the DFR process is to set the system’s reliability requirements. Reliability must be «designed in» to the system. During system design, the top– level reliability requirements are then allocated to subsystems by design engineers and reliability engineers working together.Д

Reliability design begins with the development of a model. Reliability models use block diagrams and fault trees to provide a graphical means of evaluating the relationships between different parts of the system. These models incorporate predictions based on parts–count failure rates taken from historical data. While the predictions are often not accurateИin an absolute sense, they are valuable to assess relative differences in design alternatives.

One of the most important design techniques is redundancy. This means that if one part of the system fails, there is an alternate success path, such as a backup system. An automobile brake light might use two light bulbs. If one bulb fails, the brake light still operates using the other bulb. Redundancy significantly increases system reliability, and is often the only viable means of doing so. However, redundancy is difficult and expensive, and is therefore limited to critical parts of the system. Another design technique, physics of failure, relies on understanding the physical processes of stress, strength and failure at a very detailed level. Then the material or component can be re–designed to reduce the probability of failure. Another common design technique is component derating: selecting components whose tolerance significantly exceeds the expected stress, as using a heavier gauge wire that exceeds the normal specification for the expected electrical current.

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