RELIABILITY: CONCEPTS and TRENDSby Carlos PerezWhile the word reliability is frequently used, unfortunately, the way it is used ignores its true context and real implication. With the various improvement techniques in asset improvement, the use of the reliability word has created a constant advertising siege.The most known concept to define reliability is: Probability that an asset or system operates without failing during a given period of time under some operation conditions previously established.Sometimes, this concept is wrongly used due to the particular use given to the word failure.For many, failure only means shutdowns, so they construct complex mathematical formulas to calculate shutdown probability without taking into account that a failure also occurs when being inefficient, insecure and costly, having a high rejection level, or contributing to a bad image.Other factors to be taken into account are shutdown causes that may occur for numerous reasons, so comparing apples and oranges, as the expression goes, should be avoided. An example is comparing shutdowns due to bearing lubrication with shutdowns due to errors in bearing mountings. It is not the same changing an item because it is going to fail versus changing it because it failed versus changing it because a frequency was met before it failed.Specifying an item that failed due to wearing is not the same as another that failed due to an improper installation or one damaged by an accident.Is It a Statistic Issue?A common discussion is whether or not reliability is a statistic issue. Managing data has an undeniable usefulness in the companys management and direction. It is necessary to distinguish if statistics are used to manage real data to see its behavior or to support forecasts and estimations that sometimes border on daring and irresponsible speculations.Some authors adhere to defining mathematical postulates as an absolute truth about failures and deny the fact that numbers of analyzed failures mix effects with causes. In addition, they deny that having failure data to analyze is accepting that failures occur and with more data come more failures.The most common misconception of reliability is that it is like the average time between failure occurrences. This statement has several connotations to consider. The first is to remember that the cipher is an average and the failure concept is associated with more shutdowns than with unconformities, such as spilling, a nonconforming product, or increased risks, which are failures too.Datum as such, is an average cipher. Theres a big difference between probability and reality, thus generating confusion. A probable failure is a possible failure and an occurred failure is a real failure, but a calculus logarithm doesnt necessarily assure its occurrence at a given point.Therefore, using calculated, desired, estimated, arbitrarily fixed, imagined, recommended by manuals and even invented ciphers may carry error percentages, inaccuracies and deficiencies requiring responsible handling.Getting back to boiler failures:Assume that 10 failure modes are produced within 720 hours (one month).Only two of the failure causes listed in Figure 2 produce a shutdown, generating a total of 20 shutdown hours.According to the traditional failure concept, the calculation of MTBF for the boiler would be: MTBF