摘要
In this paper, a new method is developed to model dependent failure behavior among failure mechanisms. Unlike the existing methods, the developed method models the root cause of the dependency explicitly, so that a deterministic model, rather than a probabilistic one, can be established. Three steps comprise the developed method. First, physics-of-failure（PoF） models are utilized to model each failure mechanism. Then, interactions among failure mechanisms are modeled as a combination of three basic relations, competition, superposition and coupling. This is the reason why the method is referred to as ＂compositional method＂. Finally, the PoF models and the interaction model are combined to develop a deterministic model of the dependent failure behavior. As a demonstration, the method is applied on an actual spool and the developed failure behavior model is validated by a wear test. The result demonstrates that the compositional method is an effective way to model dependent failure behavior.
In this paper, a new method is developed to model dependent failure behavior among failure mechanisms. Unlike the existing methods, the developed method models the root cause of the dependency explicitly, so that a deterministic model, rather than a probabilistic one, can be established. Three steps comprise the developed method. First, physics-of-failure（PoF） models are utilized to model each failure mechanism. Then, interactions among failure mechanisms are modeled as a combination of three basic relations, competition, superposition and coupling. This is the reason why the method is referred to as ＂compositional method＂. Finally, the PoF models and the interaction model are combined to develop a deterministic model of the dependent failure behavior. As a demonstration, the method is applied on an actual spool and the developed failure behavior model is validated by a wear test. The result demonstrates that the compositional method is an effective way to model dependent failure behavior.
基金
supported by the National Natural Science Foundation of China (No. 71671009)
supported by the National Natural Science Foundation of China (No. 61573043)
supported by the National Natural Science Foundation of China (No. 51675025)
