Aerospace-Electronics Reliability-Assurance (AERA): Three-Step Prognostics-and-Health-Monitoring (PHM) Modeling Approach

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2016 17th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)

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When encountering a particular reliability problem at the design, fabrication, testing, or an operation stage of an electronics product's life, and considering the use of predictive modeling to assess the seriousness and possible consequences of its detected malfunction and likely failure, one has to choose whether a statistical, or a physics-of-failure-based, or a suitable combination of these two major predictive modeling tools should be employed to address the problem and to decide on how to proceed. An effective aerospace-electronics reliability-assurance (AERA) approach is suggested as a possible way to go in such a situation. In this approach the classical statistical Bayes formula (BF) is used at the first step as a technical diagnostics (TD) tool, with an objective to identify, on the probabilistic basis, the faulty (malfunctioning) device(s) from the obtained prognostics-and-health-monitoring (PHM) signals ("symptoms of faults"). The physics-of-failure-based Boltzmann-Arrhenius-Zhurkov's (BAZ) equation, a powerful, flexible and physically meaningful modeling tool suggested about five years ago can be employed at the second step with an objective is to assess the remaining useful life (RUL) of the malfunctioning device(s). If the predicted RUL is still long enough, no action might be needed, but if not, a corrective (restoration) action becomes necessary. It is shown in this connection how short/long the repair time should/could be, so that the availability of the equipment (the probability that it is sound and available to the user when needed) does not fall below the allowable level. In any event, after the first two steps of the AERA modeling effort are carried out, and the assessed probability of the product's continuing operation is found to be satisfactory, the device is put back into operation (testing). If failure nonetheless occurs, the third AERA step should be undertaken to update reliability. A well-known four-parametric statistical beta-dist- ibution (BD), in which the probability of failure is treated as a random variable, can be used at this step. The general AERA concept is illustrated by a detailed numerical example geared to an en-route flight mission. The approach can be used, however, also beyond the aerospace field in other vehicular technologies: maritime, automotive, railroad, etc.



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