Data

Data is the informational energy which runs the reliability improvement machine. Data is acquired at great cost. Data needs to be retained and used to prevent future failure events. Proper use of data provides an understanding of failure mechanisms and prevents reoccurrence of bad events which cause safety or high cost failures to occur. Reliability data requires definition of a failure. Failures can be catastrophic failures or slow degradation-you decide by defining the failures. The units of the measure for the data must be in units of the degradation-sometimes it is hours, some times it is miles, and so forth-in short, what ever motivates the failure. Reliability always ceases with a failure or a removal from service in some aged condition which then generates a category of data called a suspension or censored data. Data is information in the form of facts, figures, or engineering databases which is obtained from engineering tests, experiments, or actual operating conditions. Reliability data is often incomplete as the exact times to failure are rarely known or recorded with much precision so that only partial information is available for analysis. Reliability data comes in two forms: 1) age-to-failure data, and 2) censored/suspended data such as occurs when unfailed items are removed from service or when they fail due to a different failure mode than we are studying-this is useful information and part of the data set. Some data is better than no data for resolving reliability issues.

Why: Data is the information that, when used in an informed manner, helps prevent repetition of bad history and allows an enlightened approach to rationally solving a reliability issue using facts and figures. Intelligent use of data for reliability issues provided the objective evidence needed for helping to solve the root cause of failures.

When: Databases of reliability information of past experience is very helpful for predicting future failure events. The data is helpful if failure rates, or the reciprocal of failures rates is described in mean times to failure which reduces the information to an average failure rate or average time to failure. The reliability data is particularly valuable if retained for components as a Weibull data base with shape factor beta and scale factor eta.

Where: The data is useful for understanding failure modes, and for predicting future failures for a population of equipment during the design stage and for predicting future failures with subsequent increases in the aging of equipment. The role of the reliability engineer is to acquire the failure data and convert the data into useful information for both current and future use.

These definitions are written by H. Paul Barringer and are also posted on his web site at www.barringer1.com

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