The term MTBF refers to mean time between failures. This indicates a statistical measure which facilitates in predicting the behaviour of a huge group of samples. Students can learn more about MTBF here.
Definition and Meaning of MTBF
MTBF refers to the predicted time which is elapsed between the inherent failures of an electronic and mechanical system.
Furthermore, this takes place during normal system operation. Its calculation takes place as the arithmetic mean time between the inherent failures of a system.
The definition of this concept depends on what exactly is a failure. For example, failures can be out of design conditions when it comes to complex and repairable systems.
Such conditions can place the entire system out of service. Most noteworthy, failures which do not result in placing the system out of service are not failures according to this definition.
One must understand that certain units are taken down for regular maintenance and inventory control. Moreover, such units do not fall within the definition of this failure.
Most noteworthy, the higher the MTBF, the longer a system can work before failure sets in. The process of calculating the mean time between failures is the same for various purposes.
Furthermore, it doesn’t matter whether one is evaluating a new software’s reliability or deciding on the number of spare widgets to keep in the warehouse. There are various steps that involve in the process of MTBF.
Determine the Total Time Tested
The first metric one must know is the total “unit hours “of testing. Furthermore, this testing took place in the reliability study.
Moreover, one must imagine that the subject is warehouse widgets of which 40 of them were tested for 500 hours each. Therefore, in such a case, the total unit hours which are spent testing are:
40 × 500 = 20,000 hours
Identification of the Number of Failures
The next step involves the identification of the number of failures across the entire tested population. Moreover, one must consider 10 widget failures in total.
Divide the Number of Test Hours by the Number of Failures
We already know the fact that there were 20000 total units hours of testing which took place. Furthermore, there were also 10 widget failures.
So, one must divide the total number of test hours by the number of failures. This would certainly result in the mean time between failures:
20000 unit hours ÷ 10 = 2000 unit hours
Hence, in this data model, the MTBR is certainly 2,000 unit hours.
Putting the MTBR Into Context
One must understand the proper context of a reliability equation like the MTBF before undertaking its calculation.
The mean time between failure is not meant for the prediction of a single unit’s behaviour. Most noteworthy, its main purpose is to predict the results of typical nature from a group of units.
The examples above, don’t tell that the expectation for each widget is to last 2000 hours. Rather, they tell that the average time failures occurring in the group are 2000 hours when one runs a group of widgets.
Another Statistic – The MTTR Calculation
The MTTR calculation is certainly an important statistic. Furthermore, it refers to the mean time to repair. Most noteworthy, for calculating MTTR, division of the total time spent on repairs by the number of repairs must take place.
Therefore, MTTR is:
500 hours ÷ 10 = 50 person-hours
Hence, MTTR is certainly 50 person-hours per repair.
Solved Question For You
Q.1 Which of the following steps is not a part of the MTBF calculation process:
A. Determine the Total Time Tested
B. Identification of the Number of Failures
C. Putting the MTBR Into Context
D. Breakdown of the MTBR
A1 The correct option is option D., which is “breakdown of the MTBR.” This is because “breakdown of the MTBR” it is not a part of the MTBR calculation process.