z-logo
Premium
Failure mode and effects analysis using function–motion–action decomposition method and integrated risk priority number for mechatronic products
Author(s) -
Wang Zhichao,
Ran Yan,
Yu Hui,
Jin Chuanxi,
Zhang Genbao
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2895
Subject(s) - failure mode and effects analysis , ranking (information retrieval) , analytic hierarchy process , computer science , function (biology) , mechatronics , rank (graph theory) , mathematical optimization , mathematics , reliability engineering , operations research , artificial intelligence , engineering , evolutionary biology , biology , combinatorics
Failure mode and effects analysis is a widely applied risk assessment method in various engineering and management domains. However, the identification of failure modes is difficult and uncountable. Therefore, a function–motion–action (FMA) decomposition method is developed to identify failure modes from the perspective of motion and extraordinarily suitable for mechatronic products. In the typical risk assessment, the ranking orders of failure modes are determined by risk priority number (RPN), which has been criticized for several drawbacks and improved by some alternative RPNs, but some drawbacks still exist, such as duplicate values, narrow admissible value range, and missing failure modes’ and risk factors’ weights. This study formulates several alternative weighted RPNs to overcome the above drawbacks, and the final ranking orders of failure modes are garnered through the integrated RPN (IRPN). First, failure modes are identified via the proposed FMA decomposition method and evaluated with crisp values, whose weights are aggregated from the basic failure modes’ weights. Second, the weights of the basic failure modes, risk factors and different RPN methods are derived from analytic hierarchy process. Third, the conditional weights of risk factors are determined by incorporating risk factors’ weights and failure modes’ conditional weights deduced from Shannon entropy. Next, several alternative weighted RPNs and IRPN are formulated to rank failure modes’ risk levels. Finally, an illustrative example about computer numerical control machine center is presented to demonstrate the application and effectiveness of the proposed method.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here