Mean time between failure

The mean time between failures MTBF is used as a measure of system reflabiUty, whereas the mean time to repair MTTR is taken as a measure for maintainabihty. Eor example, a system with an MTBF of 1200 h and a MTTR of 25 h would have an availabihty of 0.98. Furthermore, if only an MTBF of 800 h could be achieved, the same availabihty would be realized if the maintainabihty could be improved to the point where the MTTR was 16 h. Such trade-offs are illustrated in Figure 3, where each curve is at a constant availabihty. 

Fig. 3. System availability trade-off curves. MTBF = mean time between failures MTTR = mean time to repair.

Reliability. There has been a significant rise in interest among pump users in the 1990s to improve equipment reflabiUty and increase mean time between failures. Quantifiable solutions to such problems are being sought (61). Statistical databases (qv) have grown, improved by continuous contributions of both pump manufacturers and users. Users have also learned to compile and interpret these data. Moreover, sophisticated instmmentation has become available. Examples are vibration analysis and pump diagnostics. 

A logic model that graphically portrays the combinations of failures that can lead to a particular main failure (TOP event) or accident of interest. Given appropriate data, fault tree models can be quantitatively solved for an array of system performance characteristics (mean time between failures, probability of failure on demand, etc.)... 

Reduced maintenance costs, leading to an overall improvement in system integrity and reliability. Tlie mean-times-between-failure for active magnetic bearings are equal to tliose of an electric motor. 

The GIDEP Reliability-maintainability Data Bank (RMDB) has failure rates, failure modes, replacement rates, mean time between failure (MTBF) and mean time to repair (MTTR) on components, equipment, subsystems and systems. The RMDB includes field experience data, laboratory accelerated life test data, reliability and maintainability demonstration test results. The... 

Mean Time Between Failures (MTBFs) based on 1,116 failures... 

Repair and maintenance records were analyzed to determine failure rates and distribution of failure modes. Preliminary findings are reported which include the Weibull distribution characteristics. Failure mode distributions are approximate. Overall mean-time-between-failure is given for the kiln, leach tank, screwfeeder, tank pump, tank gearbox, and kiln gearbox. The study was confined to an analysis of unscheduled repairs and failures. 

The failure rates and times-to-restore developed used a variety of data sources and data construction methodologies and are presented in Section 2. The principal methodology used is a kind of failure mode analysis for each component several principle modes of failure are analyed by characteristics including frequency of occurence, repair time, start-up time, and shut-down time. From these an average failure rate is developed and expressed as failures per million hours and mean time between failure(MTBF). 

A side benefit of predictive maintenance is the automatic ability to monitor the mean-time-between-failures, MTBF. This data provides the means to determine the most cost-effective time to replace machinery rather than continue to absorb high maintenance costs. The MTBF of plant equipment is reduced each time a major repair or rebuild occurs. Predictive maintenance will automatically display the reduction of MTBF over the life of the machine. When the MTBF reaches the point that continued operation and maintenance costs exceed replacement cost, the machine should be replaced. 

Experience shows that some machines have more frequent failures than do others. Obviously, different failure modes have different frequencies of occurrence. This is usually described as mean-time-between-failure (MTBF) and expresses the probability of machinery failure and breakdown events as a function of time. This is of particular interest to the maintenance failure analyst and troubleshooter who have to grapple with the realization that some machinery failure modes appear slowly and predictably whilst others occur randomly and unpredictably. In most cases, both types of failures have been encountered. 

The second limitation is the life dispersion of machinery components. It is difficult to predict time-dependent failure modes because even they do not occur at the exact same operating intervals. Consider the life dispersion of mechanical gear couplings on process compressors. Both components are clearly subject to wear. If we conclude that their MTBF (mean-time between failure), or mean-time-between-reaching-of-detect-limit is 7.5 years, it is possible to have an early failure after 3 years and another... 

In an earlier section, we acquainted ourselves with commonly used reliability terms mean time between repairs (MTBR), mean time between failures (MTBF), etc. We saw that these terms have similar meanings but often include minor deviations. To avoid confusion, mean time to failure (MTTF) will be used in the following discussion. It can be expressed in terms of any time periods, i,e, days, months, years, etc. ... 

The time interval between two failures of the component is called the mean time between failures (MTBF) and is given by the first moment of the failure density function ... 

MTBF mean time between failure (time)... 

Firewater Reliability - A mathematical model of the ability of the firewater system to provide firewater upon demand as required by the design of the system without a component failure, e.g., a Mean Time Between Failure (MTBF) analysis. 

Reliability is a function the system failure rate or its reciprocal, mean time between failures (MTBF). The system failure rate in non-redundant systems is numerically equal to the sum of component failure rates. 

Change in key indicators, i.e., mean time between failure, overdue inspections, reduced equipment availability... 

The good recoveries reported above by some earlier workers in SFE clearly demonstrate the feasibility and comparative accuracy of SFE as a sample preparation technique for a variety of analytes and matrices. However, the question of the "robustness" of SFE as an analytical tool easily used on a routine basis by chemists throughout the analytical community remains to be answered. What is the expected variability of results for any given application and how much user interaction is required to maintain functional equipment sample after sample Such information - precision and mean-time-between-failures (or mean-time-between-maintenance) - has not been routinely reported in published literature since the emphasis has heretofore been on initial feasibility experiments. 

In the area of fuel cells, reliability and availability have much improved. Recent U.S. military experience with phosphoric acid fuel cells found that the mean time between failure (MTBF) was almost 1,800 h and the availability was 67%. This is comparable with the MTBF service intervals for diesel generators. These fuel cells also favorably compare with the service interval needed for a typical gas turbine generation set. Still, much more development is required to obtain a commercially viable product. Today, the typical fuel cell system still requires servicing every 3-4 days to replace its scrubber packs. 

MCFC Molten carbonate fuel cell MTBF Mean time between failures mW or MW Megawatts... 

Second, guaranteeing availability as high as 99-9999 percent requires a tremendous amount of performance data on every single piece of equipment. Accurate risk-assessment analysis requires reliable data such as mean time between failure (how long a component is likely to run before breaking down) and mean time to repair (how long it will take to fix a component that has broken down). Analysts would prefer to have as much as i million hours of data on each and every system component. That takes years to... 

For any solution, a performance expectation such as "reliability" would likely be tracked with a metric called "mean time between failures." This metric, in turn, is included in your Design Scorecards (Technique 39) for that solution. 

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