齿轮传动系统损伤检测与多故障分类研究

研究了数据挖掘的支持向量机的智能故障检测与诊断方法。通过对齿轮系统在不同的运转状态下的工作状况进行试验测试分析，获取了有关的测试信号，并对不同的故障振动特征信号进行了特征提取与分析研究。在此基础上将支持向量机引入到齿轮传动的损伤检测与诊断之中，建立了两分类和多分类分类器，研究了支持向量机的两分类和多类分类算法。通过分析处理、训练和测试仿真数据以及齿轮振动特征信号，对齿轮系统在各种不同转速下不同故障进行了预测、分类和诊断。研究表明, 支持向量机能够很好的区分不同运转状况下各种典型齿轮损伤与故障，低转速下识别率更高，为95%，特别是对各种复合类故障具有较高的识别精度、识别率在81%以上。它在齿轮故障诊断中具有较好诊断识别能力与发展前景，是一种有效地损伤检测与诊断新方法。

Damage detection and multi-faults classification of gear transmission system

A method of intelligent fault detection and diagnosis based on the Support Vector Machine (SVM) is proposed. By measuring the vibration signals of the gear system at different rotating speed for different condition and faults, the testing signals were obtained. The feature signals of system were extracted and analyzed. SVM was used for the gear fault diagnosis, the classifiers of two and multi-classification were set up, and the algorithms of two and multi-classification of SVM were discussed. After analyzing, training and testing the samples of simulation data and gear vibration signal, the various damages in different running condition for gear system were detected, classified and diagnosed. Based on these, the various representative gear damage in different condition can be distinguished very well, the detection ratios is more higher as 95% in low rotating speed, and especially the identification ratios of Multi-Faults diagnosis are over 81%. As a result, Support Vector Machine in gear fault diagnosis has well diagnostic and identify abilities and development prospects, and it is an effective new method for damage detection and fault diagnosis used in engineering.