流体机械
流體機械
류체궤계
FLUID MACHINERY
2014年
7期
43-46
,共4页
王金东%代梅%夏法锋%赵海峰
王金東%代梅%夏法鋒%趙海峰
왕금동%대매%하법봉%조해봉
经验模态分解%信息熵%支持向量机%往复压缩机%故障诊断
經驗模態分解%信息熵%支持嚮量機%往複壓縮機%故障診斷
경험모태분해%신식적%지지향량궤%왕복압축궤%고장진단
EMD%information entropy%SVM%reciprocating compressor%fault diagnosis
往复压缩机工况恶劣、结构复杂、易损件多等特点，增加了压缩机故障诊断难度。将EMD信息熵和支持向量机（SVM）技术相结合，应用于压缩机轴承故障诊断。通过EMD对压缩机轴承信号进行分解，计算其信息熵值，并提取出能反映轴承工作状态的信息熵，将其作为特征向量训练SVM网络。结果表明，EMD信息熵和支持向量机相结合的方法，可以准确识别压缩机轴承故障。
往複壓縮機工況噁劣、結構複雜、易損件多等特點，增加瞭壓縮機故障診斷難度。將EMD信息熵和支持嚮量機（SVM）技術相結閤，應用于壓縮機軸承故障診斷。通過EMD對壓縮機軸承信號進行分解，計算其信息熵值，併提取齣能反映軸承工作狀態的信息熵，將其作為特徵嚮量訓練SVM網絡。結果錶明，EMD信息熵和支持嚮量機相結閤的方法，可以準確識彆壓縮機軸承故障。
왕복압축궤공황악렬、결구복잡、역손건다등특점，증가료압축궤고장진단난도。장EMD신식적화지지향량궤（SVM）기술상결합，응용우압축궤축승고장진단。통과EMD대압축궤축승신호진행분해，계산기신식적치，병제취출능반영축승공작상태적신식적，장기작위특정향량훈련SVM망락。결과표명，EMD신식적화지지향량궤상결합적방법，가이준학식별압축궤축승고장。
The bad work conditions ,complicated structure and more easy-wearied parts,etc.,which add to the difficulties of fault diagnosis for reciprocating compressor .A compound fault diagnosis technique based on EMD-Information Entropy and Sup-port Vector Machine (SVM)are applied to fault diagnosis of reciprocating compressor bearings .The signals of reciprocating com-pressor bearings were divided by the method of Empirical Mode Decomposition .The Information Entropy was calculated and the characteristics which represent the compressor working condition were extracted .The Information Entropy can be as a vector to train Support Vector Machine Network .The results show that the method of combining EMD-Information Entropy and Support Vec-tor Machine can accurately identify failure of compressor bearings .
