Model-driven degradation modeling approaches: Investigation and review
Model-driven degradation modeling approaches: Investigation and review
复制标题
模型驱动的退化建模方法:调查和审查
DOI:
10.1016/j.cja.2019.12.006
复制
发表时间:
2020-04
影响因子:
5.7
通讯作者:
Chen Yunxia
中科院分区:
文献类型:
--
作者:
Kang Rui;Gong Wenjun;Chen Yunxia
The second law of thermodynamics implies that any animate and inanimate systems degrade and inevitably stops functioning. It is irreversible over time that can be labeled as “the degradation arrow of time”. From perspective of products’ reliability design, it is essential to build appropriate models of describing the degradation arrow of time. The current modeling approaches mainly include the model-driven (having assumed forms based on cognitive experience of mankind) and data-driven (using data learning techniques without form hypothesis) approaches. In this paper, we just investigate and review the model-driven degradation approaches, hoping to provide suggestions of the model construction or selection for scholars or engineers. First, for the single mechanism, degradation law models and stochastic process models are classified as separately depicting the tendency and fluctuation of degradation. For the degradation law model, we propose the concept of meta-models as original types for various personal models. For the stochastic process model, two main types including the non-monotonic and monotonical types are presented. Then, four multi-mechanism degradation types are discussed, that are competitive degradation, multi-stage degradation, coexistence of degradation and impact, and coexistence of degradation and failure. Besides, for the multi-performance degradation, independent and coupling models are introduced. The forms, connotations, applicability and insufficiency of these models are described with a series of examples from the literature and our own experiences. The final explicit suggestions about the potential future work are provided for the development of new degradation models.
登录
查看更多内容
影响因子:
5.9
作者:
Z. Ye;L. Tang;Haiyan Xu
通讯作者:
Z. Ye;L. Tang;Haiyan Xu
影响因子:
2.5
作者:
Ye, Zhi-Sheng;Chen, Nan
通讯作者:
Chen, Nan
DOI:
10.1017/cbo9781139626514
发表时间:
2014-02
期刊:
--
影响因子:
--
作者:
R. Gallager
通讯作者:
R. Gallager
DOI:
--
发表时间:
2005
期刊:
High Power Laser and Particle Beams
影响因子:
--
作者:
Zhao Jian-yin
通讯作者:
Zhao Jian-yin
影响因子:
2.3
作者:
Xiaolin Wang;P. Jiang;B. Guo;Z. Cheng
通讯作者:
Xiaolin Wang;P. Jiang;B. Guo;Z. Cheng