Research on rolling bearing fault diagnosis based on multi-dimensional feature extraction and evidence fusion theory.

ABSTRACT: Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstl

SUBMITTER: Li J 

PROVIDER: S-EPMC6408408 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature