基于融合信息(火用)的转子振动故障SVM诊断方法

通过提取信息(火用)特征，提出基于融合信息(火用)的转子振动故障支持向量机（SVM）诊断方法.首先，在转子试验台上分别模拟转子不平衡、轴系不对中、转子裂纹和转子碰磨4种典型故障，采集这4种典型故障在多转速和多测点下的振动加速度信号；其次，提取基于时域的奇异谱熵和频域的功率谱熵的转子振动故障过程变化规律的信息(火用)特征；最后，将提取到的信息(火用)特征作为故障向量，建立SVM故障诊断模型，进而对转子振动故障进行诊断.实例诊断结果表明：将信息(火用)特征与支持向量机相结合进行转子振动故障诊断，诊断结果准确率达到了97%，有效地提高了故障诊断的准确率.

SVM diagnosis method of rotor vibration faults based on integration of information exergy

Through extracting the information exergy characteristics, the support vector machine (SVM) diagnosis method of rotor vibration faults based on integration of information exergy was put forward. First of all, the rotor unbalance fault, shafts misalignment fault, rotor crack fault and rubbing fault were simulated respectively on the rotor test bench, and vibration acceleration signals of these four typical kinds of faults under multi-point was gathered; secondly, the information exergy characteristics of the rotor vibration fault process change law based on time-domain singular spectrum entropy and frequency-domain power spectrum entropy was extracted; finally, using the extracted information exergy characteristic was used as a fault vector, the SVM fault diagnosis model was established, and the rotor vibration fault was diagnosed. The diagnostic results of examples indicate that the use of information exergy characteristic combined with SVM in rotor vibration fault diagnosis make the diagnosis accuracy rate reach 97% improving the accuracy of diagnosis effectively.