Application of integrated factor evaluation-analytic hierarchy process-T-S fuzzy fault tree analysis in reliability allocation of industrial robot systems

Aiming at the defects (only two kinds of state, i.e., normal or fault) of existed reliability allocation methods without considering the intermediate degradation process, a methodology named integrated factor evaluation-analytic hierarchy process-T-S fuzzy fault tree analysis (IFE-AHP-T-S fuzzy FTA) is proposed to allocate the reliability index of industrial robot systems (IRSs) which have multiple fault states. Firstly, the reliability model of IRSs is established and the allocation principle of reliability index is discussed. Secondly, two-layer IFE model is established considering the degradation of mechanical structure and multi-state fault of IRSs to evaluate the technical merit of different subsystems. The hesitant fuzzy language set is adopted to reduce the subjectivity of expert evaluation process, which have the ability of dealing with uncertain information. Then, the AHP is proposed to allocate different weights for influence factors, and the T-S fuzzy FTA is presented to calculate fault probability and mean time between failures (MTBF) in the process of weight allocation of reliability index for IRSs and six subsystems. Finally, multi-state reliability index allocation of IRSs is completed. This investigation has a significance for reducing the fault probability and unsafe factors, and provides a theoretical basis for the whole life cycle design of IRSs. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

A new methodology named integrated factor evaluation-analytic hierarchy process-T-S fuzzy fault tree analysis (IFE-AHP-T-S fuzzy FTA) is proposed to allocate the reliability index of industrial robot systems (IRSs) with multiple fault states, considering the degradation of mechanical structure and multi-state fault. The study is significant for reducing fault probability and providing a theoretical basis for the whole life cycle design of IRSs.