Mean-time-between-repairs

Unavailability = I availability. If p >A, it is, asymptotically, found from equation 2.5-43 to be equation 2.5-45, where t -- 1/p is the mean time between repairs. 

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 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.

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... 

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. 

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... 

Many of the equipment vendors have developed cost of ownership (COO) models, some traceable, at lease in part, to SEMATECH. These COO models may be used to account for all aspects of amortized costs and provide a user with a highly accurate anticipated cost schedule. At a minimum a COO model should include the cost of the system, utilities, facilitization, mean-time-between-failures, mean-time-to-repair, preventative maintenance, personnel, all consumable safety costs (including that of required support equipment), reactant, and substrate costs. Each of these parameters" should be well defined and guaranteed, and the user of such models should precisely understand how up-time, mean-time-to-repair, and other terms are defined. A 90% uptime schedule is useless if the system is routinely defined to be out of service, for maintenance, 25 % of the time. 

Mean time between failures. Mean time to repair. Medium voltage. 

MTBF—mean time between failures MTTR—mean time to repair FMEA—failure mode effect analysis Uptime of equipment or downtime avoided... 

Sometimes the term mean time between failures (MTBF) when the product can be repaired or renewed is also used to denote E T. The problem with using only the MTTF as an indicator of... 

Future versions of this application will also consider dynamic risk factors such as user error, mean-time-between failure (MTBF), device failure within 30 days of a preventive maintenance or repair, and the number of years beyond the American Hospital Association s recommended useful life. 

Availability, in general, is defined as the ability of the plant/equipment to perform its required function over a stated period of time. Maintainability is the probability that a failed item can be restored to operation effectiveness within a given period of time when repair action is performed as per the specified procedure (Smith, 2011). Software is available for performing RAM studies. For smaller projects, spreadsheets can be used. Reliability and process safety are interlinked, and so combined RAM and safety (RAMS) studies can be performed with the RAMS software (Sikos and Klemes, 2010). It considers many factors affecting the plant performance such as equipment performance, redundancy, demand requirements and logistics. RAM analysis is based on statistical failure data such as mean time between failures (MTBF), mean time to repair (MTTR), mean time to failure (MTTF) and mean down time (MDT). Wherever possible, failure data available within the company should be used for RAM/RAMS study. If not, typical failure data available in the literature/software can be used. 

Mean Time Between Failures (MTBF)—MTBF is defined as the average time period of a failure/repair cycle. It includes time to failure, any time required to detect the failure, and actual repair time. This implies that a component has failed and then has been successfully repaired. For a simple repairable component. 

The expected time that the system is being executed after a repair activity until a new failure occurs is denoted MTTF (mean time to failure), and the expected time between two consecutive failures is denoted MTBF (mean time between failures). The two last terms (MTTF and MTBF) are dependent on the remaining number of software faults in the system. 

In (Gang 2008) two different methods - the direct simulation (DS) and the conditional expectation estimation (CEE) - are presented for the purpose of estimating the unreliability, transient unavailability, steady unavailability, mean time to failure (MTTF), mean time between failure (MTBF) and their parameter sensitivities of consecutive-k-out-of- F repairable systems with (k — 1) - step Markov dependence. 

Intuitively, the time between repairs will converge to some value as the number of spot repairs increases. This may be easily demonstrated by means of a Monte Carlo simulation as will be done in the examples later on. In the long run the ages of all spots become sufficiently mixed for some form of stationarity to arise. In this paper, the full probabUity distribution function of the time between repairs will be derived. 

Let a repair be performed when the coating ink = pn cells is damaged. Here p, with 0 < p < 1, is a fraction which is defined such that k = 1,2,... If the number of source processes n is sufficiently large, we may approximate the superposed process with a Poisson process with rate n/p, where p is the mean time between failures in the source processes. However, once a cell becomes damaged, the source process actually stops and the rate of the superposed process is reduced by /p. This means that the superposed process is a continuous-time Markov process which has a rate in state i equal to... 

Note that the rate defined in Eq. (12) is shghtly greater than the rate nj[i which we used here. Looking at Fig. 4, we conclude that the approximation overestimates the true time between repairs. The approximated superposition process with failure rate //r is too slow. By this we mean that, in reality, the failure of cells occurs at a slightly higher rate. From this, we conclude that the approximation will improve if we use the rate in Eq. (12), which was proposed by Torab and Kamen (2001). However, since this rate is not constant, we will not be able to use the hypoexpo-nential distribution in Eq. (15). This is because the... 

The simulation experiments are made for the serial mechanical system with six elements. The mean time between the faults and the mean time to repair is of exponential probability distribution. 

Figure 5. Dependence of unavailability Q on mean time between tests (in hours) at two values of mean time to repair (24 and 48 hours)... 

The reliability analysis output is often expressed as a percentage, which, given the specifically defined conditions, provides the confidence level that the system will not fail. Another commonly used output of the reliability analysis is the Mean Time Between Failure (MTBF), which indicates how long a system is expected to function without the need for repair [52]. 

MTBF stands for mean time between failures. It is defined as inverse of failure rate. Actually it is inverse of failure rate minus mean time to repair (MTTR). If MTTR is small, then MTBF = (1/Failure Rate). 

---