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MTBF, MTBR, MTTR, MTTF, MTBDE and FIT | Ivan Luizio Magalhães | Pulse | LinkedIn
MTBF, MTBR, MTTR, MTTF, MTBDE and FIT Publicado el 30 de marzo de 2015
Ivan Luizio Magalhães
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Mean Time Between Failure (MTBF) is a reliability term used to provide the amount of failures per million hours for a product. This is the most common inquiry about a product’s life span, and is important in the decisionmaking process of the end user. MTBF is more important for industries and integrators than for consumers. Most consumers are price driven and will not take MTBF into consideration, nor is the data often readily available. On the other hand, when equipment such as media converters or switches must be installed into mission critical applications, MTBF becomes very important. In addition, MTBF may be an expected line item in an RFQ (Request For Quote). Without the proper data, a manufacturer’s piece of equipment would be immediately disqualifi ed. Mean Time To Repair (MTTR) is the time needed to repair a failed hardware module. In an operational system, repair generally means replacing a failed hardware part. Thus, hardware MTTR could be viewed as mean time to replace a failed hardware module. Taking too long to repair a product drives up the cost of the installation in the long run, due to down time until the new part arrives and the possible window of time required to schedule the installation. To avoid MTTR, many companies purchase spare products so that a replacement can be installed quickly. Generally, however, customers will inquire about the turnaround time of repairing a product, and indirectly, that can fall into the MTTR category. Mean Time Between Replacements (MTBR) is usually used for nonrepairable components or subsystems in a repairable system. For example, a light bulb in a machine is replaced after every Tp hours of operation or replaced at failure. The mean
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MTBF, MTBR, MTTR, MTTF, MTBDE and FIT | Ivan Luizio Magalhães | Pulse | LinkedIn
time between replacements metric describes the average time between two consecutive replacements under these conditions. Mean Time To Failure (MTTF) is a basic measure of reliability for nonrepairable systems. It is the mean time expected until the fi rst failure of a piece of equipment. MTTF is a statistical value and is meant to be the mean over a long period of time and a large number of units. Technically, MTBF should be used only in reference to a repairable item, while MTTF should be used for nonrepairable items. However, MTBF is commonly used for both repairable and nonrepairable items. Mean Time Between Downing Event (MTBDE) describes the expected time between two consecutive downing events for a repairable system. Failure In Time (FIT) is another way of reporting MTBF. FIT reports the number of expected failures per one billion hours of operation for a device. This term is used particularly by the semiconductor industry but is also used by component manufacturers. FIT can be quantified in a number of ways: 1000 devices for 1 million hours or 1 million devices for 1000 hours each, and other combinations. FIT and Confidence Limits (CL) are often provided together. In common usage, a claim to 95 % confidence in something is normally taken as indicating virtual certainty. In statistics, a claim to 95 % confidence simply means that the researcher has seen something occur that only happens one time in twenty or less. For example, component manufacturers will take a small sampling of a component, test x number of hours, and then determine if there were any failures in the test bed. Based on the number of failures that occur, the CL will then be provided as well.
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Another availability indicator we measure at Adobe is Mean Time To Detect (MTTD). This is the difference between the onset of revenue impacting event and its actual detection (either by auto monitoring or manual reporting by business/technical teams). A㒙�er which an Incident (or trouble ticket) is created for technician to work on, from where MTTR clock starts. Recomendar
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MTBF, MTBR, MTTR, MTTF, MTBDE and FIT | Ivan Luizio Magalhães | Pulse | LinkedIn
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