摘要
针对齿轮的故障诊断问题,引入模糊熵的方法对齿轮振动信号进行分析。通过研究嵌入维数和延迟时间对信号模糊熵的影响,提出多维度模糊熵的齿轮故障特征提取方法。利用多维度模糊熵特征提取方法提取故障特征,并结合支持向量机建立了齿轮故障诊断模型。对实测齿轮故障数据进行分析,证明了多维度模糊熵方法可以有效提取齿轮不同状态的特征信息,与支持向量机结合可以精确地诊断齿轮典型故障,具有一定的优势。
Aiming at the fault diagnosis problem of gear, the fuzzy entropy （FE） was used to analyze gear vibration signal. Through researching the impact of embedding dimension and delaying time on signal PE, feature extraction method based on multi-dimension FE was proposed. The diagnosis model was established to diagnosis gear faults according to the support vector machine （SVM） and the feature calculated by the proposed method. The gear fault data with different fault were analyzed and the results shown that multi-dimension FE method for extracting the features is effective and the combination with SVM can diagnosis gear different fauhs accurately, and has a certain advantage.
出处
《机械设计与研究》
CSCD
北大核心
2017年第6期67-70,共4页
Machine Design And Research
基金
陕西省教育厅科学研究项目(16JK2219)
关键词
多维度
模糊熵
齿轮
故障诊断
multi-dimension
fuzzy entropy
gear
fault diagnosis
