HHT相对谱熵及其在轴承退化状态识别中的应用
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摘要
为有效地对轴承退化状态进行识别,对轴承退化特征提取方法进行了研究。基于HHT变换的非平稳信号分析能力和相对熵可以较好表征振动信号概率分布差异的特性,提出基于HHT相对谱熵的轴承退化特征提取方法。通过仿真信号对定义的HHT相对能谱熵(HREE)和HHT相对奇异谱熵(HRQE)的合理性和有效性进行了验证。将这两个特征指标组成退化特征,对实测轴承内圈和外圈故障模式下的不同程度故障振动信号进行了进一步分析,并通过相关向量机对轴承退化状态进行了识别,结果验证了所提方法的有效性。
In order to better identification the degradation state of bearing, the degradation feature extraction method of bearing degradation state are studied. A degradation state feature extraction method for bearing named HHT relative spectrum entropy is proposed based on HHT for analyzing non-stationary vibration signal and relative entropy for characterizing the probability distribution difference among different signals. The analysis results of simulation signal demonstrate the availability and relationality of the proposed HHT relative energy spectrum entropy(HREE) and HHT relative singular spectrum entropy(HRQE) used as degradation feature. The degradation feature vector is composed of the two feature. The practical vibration of bearing with inner race fault and outer race fault which in different degradation state are analyzed, and the relevance vector machine is further used to identification degradation state and the results demonstrate the ability of the proposed method.
In order to better identification the degradation state of bearing, the degradation feature extraction method of bearing degradation state are studied. A degradation state feature extraction method for bearing named HHT relative spectrum entropy is proposed based on HHT for analyzing non-stationary vibration signal and relative entropy for characterizing the probability distribution difference among different signals. The analysis results of simulation signal demonstrate the availability and relationality of the proposed HHT relative energy spectrum entropy(HREE) and HHT relative singular spectrum entropy(HRQE) used as degradation feature. The degradation feature vector is composed of the two feature. The practical vibration of bearing with inner race fault and outer race fault which in different degradation state are analyzed, and the relevance vector machine is further used to identification degradation state and the results demonstrate the ability of the proposed method.
引文
[1] 王冰, 李洪儒, 许葆华. 基于多尺度形态分解谱熵的电机轴承预测特征提取及退化状态评估[J]. 振动与冲击, 2013, 32(22): 124-128.WANG Bing, LI HongRu, XU BaoHua. Motor bearing forecast feature extracting and degradation status identification based on multi-scale morphological decomposition spectral entropy[J]. Journal of Vibration and Shock, 2013, 32(22): 124-128 (In Chinese).
[2] 张西宁, 雷威, 李兵. 主分量分析和隐马尔科夫模型结合的轴承监测诊断方法[J]. 西安交通大学学报, 2017, 51(6): 1-8.ZHANG XiLing, LEI Wei, LI Bing. Bearing fault diagnosis method based on principal component analysis and hidden markov model[J]. Journal of Xi’an Jiaotong University, 2017, 51(6): 1-8 (In Chinese).
[3] 刘吉彪, 程军圣, 刘燕飞. 基于LCD和GMM-VPMCD混合模型的滚动轴承退化状态识别[J]. 机械强度, 2016, 38(6): 1161-1166.LIU JiBiao, CHENG JunSheng, LIU YanFei. The degradation state recognition method of rolling bearing based on LCD and GMM-VPMCD hybrid model[J]. Journal of Mechanical Strength, 2016, 38(6): 1161-1166 (In Chinese).
[4] 魏永合, 王明华. 基于EEMD和SVM的轴承退化状态识别[J]. 计算机集成制造系统, 2015, 21(9): 2475-2483.WEI YongHe, WANG MingHua. Degradation state recognition of rolling bearing based on EEMD and SVM[J]. Computer Integrated Manufacturing Systems, 2015, 21(9): 2475-2483 (In Chinese).
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[6] 李小鹏, 何正友, 武骁. 基于S变换能量相对熵的高压输电线路极性比较式纵联保护[J]. 电网技术, 2014, 38(8): 2250-2256.LI XiaoPeng, HE ZhengYou, WU Xiao. Ploarity comparsion pilot protection based on S-transform energy relative entropy for high voltage transmission lines[J]. Power Systerm Technology, 2014, 38(8): 2250-2256. (In Chinese)
[7] 张鑫, 吴亚蜂, 朱帅琦. 基于EEMD-HHT边际谱的轴承故障诊断[J]. 科学技术与工程, 2011, 11(31): 7625-7629.ZHANG Xin, WU YaFeng, ZHU ShuaiQi. Rolling bearing fault diagnosis based on ensemble empirical mode and hillbert-huang transform marginal spectrum[J]. Science Technology and Engineering, 2011, 11(31): 7625-7629 (In Chinese).
[8] 孟宗, 李珊珊. 基于小波改进阈值去噪和HHT的滚动轴承故障诊断[J]. 振动与冲击, 2013, 32(14): 204-208.MENG Zong, LI ShanShan. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT[J]. Journal of Vibration and Shock, 2013, 32(14): 204-208. (In Chinese)
[9] 吴定海, 张培林, 张英堂, 等. 基于时频奇异谱和RVM的柴油机故障诊断研究[J]. 机械强度, 2011, 33(3): 317-323.WU DingHai, ZHANG PeiLin, ZHANG YingTang, etal. Study on diesel engine faults diagnosis based on time-frequency singular value spectrum and RVM[J]. Journal of Mechanical Strength, 2011, 33(3): 317-323 (In Chinese).
[10] Yaguo Lei, Jing Lin, Zhengjia He, etal. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126.
[11] 赵洪山, 郭双伟, 高夺. 基于奇异值分解和变分模态分解的轴承故障特征提取[J]. 振动与冲击, 2016, 35(22): 183-188.ZHAO HongShan, GUO ShuangWei, GAO Duo. Fault feature extration of bearing faults based on singular value decomposition and variational modal decomposition[J]. Journal of Vibration and Shock, 2016, 35(22): 183-188 (In Chinese).
[2] 张西宁, 雷威, 李兵. 主分量分析和隐马尔科夫模型结合的轴承监测诊断方法[J]. 西安交通大学学报, 2017, 51(6): 1-8.ZHANG XiLing, LEI Wei, LI Bing. Bearing fault diagnosis method based on principal component analysis and hidden markov model[J]. Journal of Xi’an Jiaotong University, 2017, 51(6): 1-8 (In Chinese).
[3] 刘吉彪, 程军圣, 刘燕飞. 基于LCD和GMM-VPMCD混合模型的滚动轴承退化状态识别[J]. 机械强度, 2016, 38(6): 1161-1166.LIU JiBiao, CHENG JunSheng, LIU YanFei. The degradation state recognition method of rolling bearing based on LCD and GMM-VPMCD hybrid model[J]. Journal of Mechanical Strength, 2016, 38(6): 1161-1166 (In Chinese).
[4] 魏永合, 王明华. 基于EEMD和SVM的轴承退化状态识别[J]. 计算机集成制造系统, 2015, 21(9): 2475-2483.WEI YongHe, WANG MingHua. Degradation state recognition of rolling bearing based on EEMD and SVM[J]. Computer Integrated Manufacturing Systems, 2015, 21(9): 2475-2483 (In Chinese).
[5] 胡为, 胡静涛. 改进的符号时间序列分析方法和及其在电机故障诊断中的应用[J]. 仪器仪表学报, 2009, 30(4): 760-766.HU Wei, HU JingTao. Improved symbolic time series analysis method and its application in motor fault diagnosis[J]. Journal of Scientific Instrument, 2009, 30(4): 760-766 (In Chinese).
[6] 李小鹏, 何正友, 武骁. 基于S变换能量相对熵的高压输电线路极性比较式纵联保护[J]. 电网技术, 2014, 38(8): 2250-2256.LI XiaoPeng, HE ZhengYou, WU Xiao. Ploarity comparsion pilot protection based on S-transform energy relative entropy for high voltage transmission lines[J]. Power Systerm Technology, 2014, 38(8): 2250-2256. (In Chinese)
[7] 张鑫, 吴亚蜂, 朱帅琦. 基于EEMD-HHT边际谱的轴承故障诊断[J]. 科学技术与工程, 2011, 11(31): 7625-7629.ZHANG Xin, WU YaFeng, ZHU ShuaiQi. Rolling bearing fault diagnosis based on ensemble empirical mode and hillbert-huang transform marginal spectrum[J]. Science Technology and Engineering, 2011, 11(31): 7625-7629 (In Chinese).
[8] 孟宗, 李珊珊. 基于小波改进阈值去噪和HHT的滚动轴承故障诊断[J]. 振动与冲击, 2013, 32(14): 204-208.MENG Zong, LI ShanShan. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT[J]. Journal of Vibration and Shock, 2013, 32(14): 204-208. (In Chinese)
[9] 吴定海, 张培林, 张英堂, 等. 基于时频奇异谱和RVM的柴油机故障诊断研究[J]. 机械强度, 2011, 33(3): 317-323.WU DingHai, ZHANG PeiLin, ZHANG YingTang, etal. Study on diesel engine faults diagnosis based on time-frequency singular value spectrum and RVM[J]. Journal of Mechanical Strength, 2011, 33(3): 317-323 (In Chinese).
[10] Yaguo Lei, Jing Lin, Zhengjia He, etal. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126.
[11] 赵洪山, 郭双伟, 高夺. 基于奇异值分解和变分模态分解的轴承故障特征提取[J]. 振动与冲击, 2016, 35(22): 183-188.ZHAO HongShan, GUO ShuangWei, GAO Duo. Fault feature extration of bearing faults based on singular value decomposition and variational modal decomposition[J]. Journal of Vibration and Shock, 2016, 35(22): 183-188 (In Chinese).