Failure life data, defined

CCPS defines reliability as the probability that an item is able to perform a required function under stated conditions for a stated period of time or for a stated demand. In addition to relying on QA/QC, companies use dependable data to conduct reliability analyses. Though related,- reliability is different from quality. While quality control is concerned with the performance of a product or process at one time, reliability is concerned with the performance of a product over its entire lifetime. Reliability engineering addresses all aspects of a product s life, from its conception, subsequent design, and production processes, through its practical use lifetime, with maintenance support and availability, and covers reliability, maintainability, and availability. Process safety metrics data can provide valuable input to the life data analyses used to estimate the probability and capability of parts, components, and systems to perform their required functions for desired periods without failure, in specified environments. 

Acceleration Factors. One of the challenges that must be addressed when attempting to employ Eq. 59.9 directly to assess fatigue life is the determination of the plastic strain and the value of the constant yl. It is crucial to remember that the goal of this analysis is to develop an acceleration transform (or acceleration factor) that can be employed to use known fatigue-life data obtained under controlled laboratory conditions to estimate the number of cycles to failure under field conditions. The acceleration factor AF is defined as the ratio of the number of cycles to fail in the field Nfr to the number of cycles to fail in the lab NfL (see Eq. 59.10). 

The primary failure mode of the VRLA battery can be defined as growth of the positive plate. Because this growth is the result of chemical reactions within the cell, the rate of growth increases with increasing temperature. In Fig. 24.20 the float life is plotted against temperature. The solid lines represent data from float-life tests performed at two float voltages, 2.3 and 2.4 V per cell. This graph can be used to determine the expected float life at various temperatures. End of life is defined as the failure of the cell to deliver 80% of rated capacity. 

The degree of life acceleration that this test represents is uncertain. Fitting data to a temperature dependent first-order exponential (Arrhenius) failure curve is often inaccurate due to initial model assumptions. Depending upon the estimate of activation energies used, and the criteria used to define failure, an... 

A diagram that one might use to illustrate a possible set of experimental data to represent all failure modes of an adhesive joint is presented in Fig. 15.1. When the data are closely analyzed and the extent of ultimate service life and proper safety margins are specified, the critical failure mode and time can be defined by identifying the weakest link — in this case the corrosion mechanism. If this predicted life is longer than the expected service life of the product, then the material specified for the adhesive joint can be qualified for use. 

The revised structural safety parameters of existing structures help us assess their reliability indices, correct their technical service life and allow avoid both unexpected failures and unfounded premature repairs. When unfavorable service-proven action effects caused by extreme live or climate actions are defined and conformed by quality statistical information data, the revised reliability indices of members and their systems may be assessed and predicted fairly exactly by unsophisticated engineering probabilistic methods. 

Failure data are generally obtained mainly from the failure times of various items in a population placed on a life test, or repair reports or from similar plant data. Since such data are sequential discrete data, whereas probability density functions considered as continuous, it is necessary to define piecewise-continuous failure density and hazard-rate functions in terms of the data. 

A practical use of fitting a distribution to reliability data is to extrapolate to smaller failure rates or other environmental conditions. To simplify the equations, the expressions in the text refer to the mean life of the relevant portion of the assembly. If the constants that define the failure distribution are known, the time to reach a smaller proportion of failures may be readily calculated. For example, for failure modes that are described by a Weibull distribution, the time f to reach x% failures is given by ... 

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