CAJ | 학술논문

柱塞泵是工程机械的关键部件，其性能好坏将直接影响整个设备的正常工作。针对柱塞泵提出基于特征选择支持向量机的智能诊断方法。对采集的振动信号基于小波包分解提取能量特征，然后利用Fisher准则函数选择对智能诊断最有利的特征，利用支持向量机进行训练，并将每个二类支持向量机按二叉树的组织形式构成系统的诊断模型。以汽车起重机柱塞泵为研究对象，其6种故障形式，包括正常、轴承内圈故障、滚动体故障、柱塞故障、配流盘故障、斜盘故障，用于检验所提算法的诊断能力，并与传统的BP神经网络和最近的蚁群神经网络方法进行对比。诊断结果表明：所提出的算法优于另外两种方法，具有较好的诊断效果。
주새빙시공정궤계적관건부건，기성능호배장직접영향정개설비적정상공작。침대주새빙제출기우특정선택지지향량궤적지능진단방법。대채집적진동신호기우소파포분해제취능량특정，연후이용Fisher준칙함수선택대지능진단최유리적특정，이용지지향량궤진행훈련，병장매개이류지지향량궤안이차수적조직형식구성계통적진단모형。이기차기중궤주새빙위연구대상，기6충고장형식，포괄정상、축승내권고장、곤동체고장、주새고장、배류반고장、사반고장，용우검험소제산법적진단능력，병여전통적BP신경망락화최근적의군신경망락방법진행대비。진단결과표명：소제출적산법우우령외량충방법，구유교호적진단효과。
In truck crane,the plunger pump is the key equipment,and the quality of the pump affects directly the performance of whole mechanical system. A novel intelligent diagnosis method based on features selection and support vector machine (SVM)was proposed for plunger pump in truck crane. Based on the wavelet packet decompose,the wavelet packet energy was extracted from the original vibration signal to represent the condition of equipment. Then,the Fisher criterion was utilized to select the most suitable fea-tures for diagnosis. Finally,each two-class SVM with binary tree architecture was trained to recognize the condition of mechanism. The proposed method was employed in the diagnosis of plunger pump in truck crane. The six states,including normal state,bearing inner race fault,bearing roller fault,plunger fault,thrust plate wear fault,and swash plate wear fault,were used to test the classification performance of the proposed Fisher-SVMs model,which was compared with the classical and the latest models,such as BP ANN,ANT ANN,respectively. The experimental results show that the Fisher-SVMs is superior to the other two models,and gets a promising re-sult.