Dynamic of Damage of Aircraft Engine High-speed Rolling-element Bearings Tested in Low-viscosity Model Mediums

Silaev, B. M.; Fedorov, Dmitriy; Danilenko, P. A.

doi:10.1016/j.proeng.2017.02.271

Cited by 2 publications

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Fault Diagnosis Based on Tree Heuristic Feature Selection and FS-DFV for Rolling Element Bearings

chen

Liu

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

In order to make up for the deficiency of traditional single diagnosis in rolling element bearing fault diagnosis application, eliminate a large amount of redundant information and improve the classification effect of the aliasing mode, based on comprehensive analysis of the respective advantages of fuzzy set and tree search, this paper presents a joint rolling bearing fault diagnosis method based on tree-inspired feature selection and FS-DFV (Fuzzy Set and Dependent Feature Vector). The dependent feature vectors (DFV) can dig deeper the essential differences of the faults and improve the fault accuracy. By establishing the heuristic tree model, the tree type heuristic feature search strategy is designed, and the excellent feature selection criteria based on the density clustering with noise are proposed, and the conventional feature selection model is improved. In addition, fuzzy set are used to process the problem of extracting aliasing patterns in the DFV, and fuzzy membership is used to guide subsequent feature extraction of the alias modes. The proposed method is compared with the other four fault diagnosis methods. The experimental results show that the proposed method can effectively improve the diagnostic efficiency of the rolling bearing.

show abstract

Fault Diagnosis Based on Tree Heuristic Feature Selection and FS-DFV for Rolling Element Bearings

chen

Liu

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

Evaluation of bearing assembly lifespan for electric motors - a case study on agriculture

Nekrasov

Bolshev

et al. 2020

2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

View full text Add to dashboard Cite

Dynamic of Damage of Aircraft Engine High-speed Rolling-element Bearings Tested in Low-viscosity Model Mediums

Cited by 2 publications

References 2 publications

Fault Diagnosis Based on Tree Heuristic Feature Selection and FS-DFV for Rolling Element Bearings

Fault Diagnosis Based on Tree Heuristic Feature Selection and FS-DFV for Rolling Element Bearings

Evaluation of bearing assembly lifespan for electric motors - a case study on agriculture

Contact Info

Product

Resources

About