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Virtual - Researchers Catch-up host online from University of WA
Historically the lifetimes of individual units of multi-component systems are modelled as independent and identically distributed. Sequential Order Statistics relax this assumption by allowing component lifetime distributions to change upon failure of another unit. This assumption is justified when the stress on the surviving units increases, and their respective lifetime expectation consequently reduces as components fail successively. Most load-sharing systems will exhibit such a relationship between components. Take a series of wash tanks that filter an inflow of contaminated products, for example. Each tank experiences a different workload based on its position in the series. Consequently, the tanks have different lifetime expectations, even if they are of the same type. Tim will present a series of exciting inferential results for Extended Sequential Order Statistics. He will present a likelihood ratio test to help determine which model is better suited, the Sequential Order Statistics or the Extended Sequential Order Statistics model. About the presenter He expects to deliver a model that industrial partners can deploy to predict failures of their equipment components. |