Bearing Fault Diagnosis Based on BP Neural Network

2018 International Conference on Air Pollution and Environmental Engineering

生态环境科学

Lin, Huo^1,2 ; Xinyue, Zhang^1 ; Handong, Li^1

School of Safety Engineering, Shenyang Aerospace University, Shenyang, China^1

Liaoning Key Laboratory of Aircraft Safety and Airworthiness, Shenyang, China^2

关键词: Bearing fault diagnosis;    BP neural networks;    Fault characteristics;    Fault-based;    Mapping relationships;    Normal operations;    Rolling bearings;    State Detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/208/1/012092/pdf
DOI  :  10.1088/1755-1315/208/1/012092

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Rolling bearings are essential parts, and its operation directly affects the overall condition of the equipment. Due to the complicated non-mapping relationship between the fault and the symptom issued, this paper adopts the strong non-mapping of the neural network and the ability to self-learn and adapt to the state detection and fault diagnosis of the rolling bearing. Through select the fault characteristic parameters, and the BP neural network to select normal operation and inner ring fault characteristic parameters for fault diagnosis. Experiments show that the BP neural network constructed in this paper can accurately determine whether the rolling bearing is fault based on actual data.