Reliability and availability models use block diagrams and Fault Tree Analysis to provide a graphical means of evaluating the relationships between different parts of the system. These models may incorporate predictions based on failure rates taken from historical data. While the input data predictions are often not accurate in an absolute sense, they are valuable to assess relative differences in design alternatives.
Maintainability parameters, for example Mean time to repair MTTR , can also be used as inputs for such models. The most important fundamental initiating causes and failure mechanisms are to be identified and analyzed with engineering tools. A diverse set of practical guidance as to performance and reliability should be provided to designers so that they can generate low-stressed designs and products that protect, or are protected against, damage and excessive wear.
Proper validation of input loads requirements may be needed, in addition to verification for reliability "performance" by testing. 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.
The reason why this is the ultimate design choice is related to the fact that high-confidence reliability evidence for new parts or systems is often not available, or is extremely expensive to obtain. By combining redundancy, together with a high level of failure monitoring, and the avoidance of common cause failures; even a system with relatively poor single-channel part reliability, can be made highly reliable at a system level up to mission critical reliability.
No testing of reliability has to be required for this. In conjunction with redundancy, the use of dissimilar designs or manufacturing processes e. Redundancy can also be applied in systems engineering by double checking requirements, data, designs, calculations, software, and tests to overcome systematic failures. For electronic assemblies, there has been an increasing shift towards a different approach called physics of failure.
This technique relies on understanding the physical static and dynamic failure mechanisms. The material or component can be re-designed to reduce the probability of failure and to make it more robust against such variations.
Another common design technique is component derating : i. Many of the tasks, techniques, and analyses used in Reliability Engineering are specific to particular industries and applications, but can commonly include:. Results from these methods are presented during reviews of part or system design, and logistics.
Reliability is just one requirement among many for a complex part or system. Engineering trade-off studies are used to determine the optimum balance between reliability requirements and other constraints. Reliability engineers, whether using quantitative or qualitative methods to describe a failure or hazard, rely on language to pinpoint the risks and enable issues to be solved. Systems engineering is very much about finding the correct words to describe the problem and related risks , so that they can be readily solved via engineering solutions. Jack Ring said that a systems engineer's job is to "language the project.
Understanding "why" a failure has occurred e. This is partly done in pure language and proposition logic, but also based on experience with similar items. This can for example be seen in descriptions of events in fault tree analysis , FMEA analysis, and hazard tracking logs. In this sense language and proper grammar part of qualitative analysis plays an important role in reliability engineering, just like it does in safety engineering or in-general within systems engineering.
Correct use of language can also be key to identifying or reducing the risks of human error , which are often the root cause of many failures. This can include proper instructions in maintenance manuals, operation manuals, emergency procedures, and others to prevent systematic human errors that may result in system failures. These should be written by trained or experienced technical authors using so-called simplified English or Simplified Technical English , where words and structure are specifically chosen and created so as to reduce ambiguity or risk of confusion e.
Reliability modeling is the process of predicting or understanding the reliability of a component or system prior to its implementation. Two types of analysis that are often used to model a complete system's availability behavior including effects from logistics issues like spare part provisioning, transport and manpower are Fault Tree Analysis and reliability block diagrams. At a component level, the same types of analyses can be used together with others. The input for the models can come from many sources including testing; prior operational experience; field data; as well as data handbooks from similar or related industries.
Regardless of source, all model input data must be used with great caution, as predictions are only valid in cases where the same product was used in the same context. As such, predictions are often only used to help compare alternatives. Software reliability is a more challenging area that must be considered when computer code provides a considerable component of a system's functionality.
Reliability is defined as the probability that a device will perform its intended function during a specified period of time under stated conditions. Mathematically, this may be expressed as,. Quantitative requirements are specified using reliability parameters. The most common reliability parameter is the mean time to failure MTTF , which can also be specified as the failure rate this is expressed as a frequency or conditional probability density function PDF or the number of failures during a given period.
These parameters may be useful for higher system levels and systems that are operated frequently i. Reliability increases as the MTTF increases. The MTTF 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, as often used in system safety engineering. A special case of mission success is the single-shot device or system. These are devices or systems that remain relatively dormant and only operate once. Examples include automobile airbags , thermal batteries and missiles.
Single-shot reliability is specified as a probability of one-time success or is subsumed into a related parameter. Single-shot missile reliability may be specified as a requirement for the probability of a hit.
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For such systems, the probability of failure on demand PFD is the reliability measure — this is actually an "unavailability" number. The PFD is derived from failure rate a frequency of occurrence and mission time for non-repairable systems. For repairable systems, it is obtained from failure rate, mean-time-to-repair MTTR , and test interval.
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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 most cases, reliability parameters are specified with appropriate statistical confidence intervals. 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.
Reliability testing may be performed at several levels and there are different types of testing. Complex systems may be tested at component, circuit board, unit, assembly, subsystem and system levels. For example, performing environmental stress screening tests at lower levels, such as piece parts or small assemblies, catches problems before they cause failures at higher levels.
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Testing proceeds during each level of integration through full-up system testing, developmental testing, and operational testing, thereby reducing program risk. However, testing does not mitigate unreliability risk. With each test both a statistical type 1 and type 2 error could be made and depends on sample size, test time, assumptions and the needed discrimination ratio. There is risk of incorrectly accepting a bad design type 1 error and the risk of incorrectly rejecting a good design type 2 error.
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, different approaches to testing can be used, such as highly accelerated life testing, design of experiments , and simulations. The desired level of statistical confidence also plays a 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. The customer and developer should 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. As part of the requirements phase, the reliability engineer develops a test strategy with the customer.
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The test strategy makes trade-offs between the needs of the reliability organization, which wants as much data as possible, and constraints such as cost, schedule and available resources. Test plans and procedures are developed for each reliability test, and results are documented.
Reliability testing is common in the Photonics industry. Examples of reliability tests of lasers are life test and burn-in. These tests consist of the highly accelerated aging, under controlled conditions, of a group of lasers. The data collected from these life tests are used to predict laser life expectancy under the intended operating characteristics.
Reliability test requirements can follow from any analysis for which the first estimate of failure probability, failure mode or effect needs to be justified. Evidence can be generated with some level of confidence by testing. With software-based systems, the probability is a mix of software and hardware-based failures. Testing reliability requirements is problematic for several reasons. A single test is in most cases insufficient to generate enough statistical data.
Multiple tests or long-duration tests are usually very expensive. Some tests are simply impractical, and environmental conditions can be hard to predict over a systems life-cycle. Reliability engineering is used to design a realistic and affordable test program that provides empirical evidence that the system meets its reliability requirements. Statistical confidence levels are used to address some of these concerns.
From this specification, the reliability engineer can, for example, design a test with explicit criteria for the number of hours and number of failures until the requirement is met or failed. Different sorts of tests are possible. The combination of required reliability level and required 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—e. Reliability testing may be performed at various levels, such as component, subsystem and system. Also, many factors must be addressed during testing and operation, such as extreme temperature and humidity, shock, vibration, or other environmental factors like loss of signal, cooling or power; or other catastrophes such as fire, floods, excessive heat, physical or security violations or other myriad forms of damage or degradation.
For systems that must last many years, accelerated life tests may be needed. The purpose of accelerated life testing ALT test is to induce field failure in the laboratory at a much faster rate by providing a harsher, but nonetheless representative, environment. In 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:. Software reliability is a special aspect of reliability engineering.
System reliability, by definition, includes all parts of the system, including hardware, software, supporting infrastructure including critical external interfaces , operators and procedures. Traditionally, reliability engineering focuses on critical hardware parts of the system.
Since the widespread use of digital integrated circuit technology, software has become an increasingly critical part of most electronics and, hence, nearly all present day systems. There are significant differences, however, in how software and hardware behave. Most hardware unreliability is the result of a component or material failure that results in the system not performing its intended function.
Repairing or replacing the hardware component restores the system to its original operating state. However, software does not fail in the same sense that hardware fails. Instead, software unreliability is the result of unanticipated results of software operations. Even relatively small software programs can have astronomically large combinations of inputs and states that are infeasible to exhaustively test.
Restoring software to its original state only works until the same combination of inputs and states results in the same unintended result. Software reliability engineering must take this into account. Despite this difference in the source of failure between software and hardware, several software reliability models based on statistics have been proposed to quantify what we experience with software: the longer software is run, the higher the probability that it will eventually be used in an untested manner and exhibit a latent defect that results in a failure Shooman , Musa , Denney As with hardware, software reliability depends on good requirements, design and implementation.
Software reliability engineering relies heavily on a disciplined software engineering process to anticipate and design against unintended consequences. There is more overlap between software quality engineering and software reliability engineering than between hardware quality and reliability. A good software development plan is a key aspect of the software reliability program. The software development plan describes the design and coding standards, peer reviews , unit tests , configuration management , software metrics and software models to be used during software development. A common reliability metric is the number of software faults, usually expressed as faults per thousand lines of code.
This metric, along with software execution time, is key to most software reliability models and estimates. The theory is that the software reliability increases as the number of faults or fault density decreases or goes down. Establishing a direct connection between fault density and mean-time-between-failure is difficult, however, because of the way software faults are distributed in the code, their severity, and the probability of the combination of inputs necessary to encounter the fault. Nevertheless, fault density serves as a useful indicator for the reliability engineer.
Other software metrics, such as complexity, are also used. This metric remains controversial, since changes in software development and verification practices can have dramatic impact on overall defect rates.
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Testing is even more important for software than hardware. Even the best software development process results in some software faults that are nearly undetectable until tested. As with hardware, software is tested at several levels, starting with individual units, through integration and full-up system testing. Unlike hardware, it is inadvisable to skip levels of software testing. During all phases of testing, software faults are discovered, corrected, and re-tested.
Reliability estimates are updated based on the fault density and other metrics. At a system level, mean-time-between-failure data can be collected and used to estimate reliability. Unlike hardware, performing exactly the same test on exactly the same software configuration does not provide increased statistical confidence.
Instead, software reliability uses different metrics, such as code coverage. Eventually, the software is integrated with the hardware in the top-level system, and software reliability is subsumed by system reliability. The Software Engineering Institute's capability maturity model is a common means of assessing the overall software development process for reliability and quality purposes. Structural reliability or the reliability of structures is the application of reliability theory to the behavior of structures. It is used in both the design and maintenance of different types of structures including concrete and steel structures.
Using this approach the probability of failure of a structure is calculated. Reliability engineering is concerned with overall minimisation of failures that could lead to financial losses for the responsible entity, whereas safety engineering focuses on minimising a specific set of failure types that in general could lead to large scale, widespread issues beyond the responsible entity. Reliability hazards could transform into incidents leading to a loss of revenue for the company or the customer, for example due to direct and indirect costs associated with: loss of production due to system unavailability; unexpected high or low demands for spares; repair costs; man-hours; multiple re-designs; interruptions to normal production etc.
Safety engineering is often highly specific, relating only to certain tightly regulated industries, applications, or areas. It primarily focuses on system safety hazards that could lead to severe accidents including: loss of life; destruction of equipment; or environmental damage. As such, the related system functional reliability requirements are often extremely high. Although it deals with unwanted failures in the same sense as reliability engineering, it, however, has less of a focus on direct costs, and is not concerned with post-failure repair actions.
Another difference is the level of impact of failures on society, leading to a tendency for strict control by governments or regulatory bodies e. This can occasionally lead to safety engineering and reliability engineering having contradictory requirements or conflicting choices at a system architecture level. In this example, a wrong-side failure needs an extremely low failure rate as such failures can lead to such severe effects, like frontal collisions of two trains where a signalling failure leads to two oncoming trains on the same track being given GREEN lights.
Such systems should be and thankfully are designed in a way that the vast majority of failures e. Reliability Requirements 20 2. Reliability Modeling for Systems Engineers 84 3. Reliability Modeling for Systems Engineers 4. Comparing Predicted and Realized Reliability with Requirements 5. Design for Reliability 6. Reliability Engineering for High? Reliability Engineering for Services 8. Maintainability Requirements Design for Maintainability The calculation of parameter value on RAM Analysis should be done on an equipment level, even on more detailed stage failure mode.
Analysis for equipment could help us identifying bad actors on plant or factory. If the identify process was done on failure mode stage, the collected data could also help identifies the effectiveness of a maintenance strategy. Picture 1. There are so many equipment in one plant or factory that reliability engineer will find it quite hard to decide which equipment that the maintenance strategy should be first evaluated.
One of the most helpful method is to register critical damaged equipment. Data preparation starts with identifying when will one equipment shuts down and defining the needed time to repair that equipment. Picture 2.
Scientifically, reliability parameter is measured with percentage. Weibull equation method is usually used for the measurement. On manufacture industries, deriving Weibull-Reliability curve Picture 3 is concluded by testing factory equipment until its failure. Equipment failure time data is also used on Reliability data with Weibull method, so those data show Weibull parameters that is needed for RBD process.
On doing RAM analysis, several methods are needed so the whole process could be done perfectly, such as completed tools or technologies, business process and competence. This conference is best for every reliability practitioners in order to implement best and suitable solutions that will be formulated from different success cases from different companies and industries.
Tiara Vibrasindo Pratama has big influence as the organizer of this conference. I participated to this conference a couple of times and already able to apply best practices that I got from this conference for my company.
TRUC is very well made conference, where all reliability practitioners from all different companies and industries gather to share different phenomenon through multiple technology approach. Thus, it helps all reliability practitioners become more comprehensive. Indonesia Power Maintenance Service Unit. This is a special event which is rarely happened in Indonesia and consistently giving the space and time for industry to share various techniques in PdM.
This conference held also as a benchmarking of maintenance system and reliability application maturity. Ilham B. Tiara Vibrasindo Pratama is company which consistent on Plant Reliability.