Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types

•New results on nonparametric predictive inference for system reliability.•Prediction of system reliability based on test data for components.•New insights on system redundancy optimization and diversity.•Components that appear inferior in tests may be included to enhance redundancy.

Nonparametric predictive inference for system reliability has recently been presented, with specific focus on k-out-of-m:G systems. The reliability of systems is quantified by lower and upper probabilities of system functioning, given binary test results on components, taking uncertainty about component functioning and indeterminacy due to limited test information explicitly into account. Thus far, systems considered were series configurations of subsystems, with each subsystem i a ki-out-of-mi:Gmi:G system which consisted of only one type of components. Key results are briefly summarized in this paper, and as an important generalization new results are presented for a single k-out-of-m:G system consisting of components of multiple types. The important aspects of redundancy and diversity for such systems are discussed.