噪声与振动控制2017，Vol.37Issue(3)：162-167,6.DOI:10.3969/j.issn.1006-1355.2017.03.032
基于稀疏表示和SVM的航空发动机故障诊断
Fault Diagnosis of Aircraft Engines Based on Sparse Representation and SVM
摘要
Abstract
Considering the dismantling difficulty of the reducer of an aircraft engine and the necessity of the crack detection in its first grade gear hub, a fault diagnosis method based on sparse representation and support vector machine (SVM) is proposed. Firstly, the sparse representation is used to extract the largest and the secondary largest sparse factors as the feature vectors. Then, the fault is recognized using SVM, which maintains the high recognition accuracy under small training sample capacity condition. The analysis of vibration signals from a simple reducer and an aero-engine proves the efficiency and engineering application value of the proposed method.关键词
振动与波/航空发动机/故障诊断/稀疏表示/支持向量机Key words
vibration and wave/aircraft engine/fault diagnosis/sparse representation/SVM分类
数理科学引用本文复制引用
巩孟林,陈卫,钟也磐,杜炜,李思路,梁涛..基于稀疏表示和SVM的航空发动机故障诊断[J].噪声与振动控制,2017,37(3):162-167,6.基金项目
国家自然科学基金资助项目(51175509) （51175509）
