Reliability Databases in perspective
Concepts and methods used in designing modern RDB (reliability data banks) are reviewed. Taxonomies for failures and maintenance actions are not fully standardized and can cause confusion. Section H summarizes current usages. The structure of the raw data is identified as: component socket histories with competing risk. Successive sections discuss data-structure and operations on data, data analysis with and without competing risk, and the analysis of uncertainty. In the context of reliability data, the assumption that competing risks are s-independent is frequently unwarranted. Models for dependent competing risk are discussed, and illustrated with examples from pressure-relief-valve data at two Swedish nuclear-power stations. Some lessons learned are: The needs of various users of reliability databases must be distinguished. The interests and requirements of a maintenance engineer are very different from those of a risk analyst or a component designer. It is unlikely that a single reliability number serves these diverse needs. The problem of analyzing reliability-data has become a problem of data compression. Years ago, analysts justifiably complained about the sparseness of reliability data. With the advent of computers, automated sensors, and condition monitoring, there is no reason for sparseness today. The problem is knowing what to collect, how to make sense of the wealth of data that one can gather, and what to do with it. To be useful, reliability data must reveal not only the probability of failure, but also the cause of failure. Thus, competing factors causing the termination of service sojourns must be quantifiable from reliability data. Failure-causes interact, and this interaction makes the problem of quantification very challenging.
reliability database competing risk dependent competing risk censoring colored poisson process dependent censoring maintenance model