基于特征提取与识别两阶段的汽车电机轴承故障诊断
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摘要
针对特征提取和故障识别这两个轴承故障诊断的关键环节,提出一种汽车电机轴承故障诊断新方法。该方法在特征提取环节:提出了将LCD分解和符号熵(SE)相结合的特征提取方法;在故障识别环节为提高果蝇算法(FOA)对相关向量机(RVM)参数的寻优能力,在FOA算法中增加了向"历史"学习的策略,提出具有历史学习能力的果蝇算法(HSAFOA),有效地提升了RVM的分类性能。汽车电机轴承不同类型、不同程度故障诊断实例表明,LCD符号熵可有效对故障进行表征,而HSAFOA算法则提升了RVM的识别效果,相比于其他一些方法更有优势。
Aiming at the two key points(feature extraction and fault recognition) of bearing fault diagnosis, a new car motor bearing fault diagnosis method was proposed. At the feature extraction link: a feature extraction method based on LCD decomposition and symbol entropy was proposed. At the fault identification link: in order to improve search ability of fruit fly optimization algorithm(FOA) to relevance vector machine(RVM), study of "history" strategy was introduced to FOA, then, FOA with history study ability(HSAFOA) was proposed and effectively improved the classification performance of RVM. Different fault types and different fault degrees of rolling bearing fault diagnosis experiment results show that the LCD symbol entropy can represent fault effectively and HSAFOA improved the identification accuracy of RVM, it has a certain superiority when compared with some other methods.
Aiming at the two key points(feature extraction and fault recognition) of bearing fault diagnosis, a new car motor bearing fault diagnosis method was proposed. At the feature extraction link: a feature extraction method based on LCD decomposition and symbol entropy was proposed. At the fault identification link: in order to improve search ability of fruit fly optimization algorithm(FOA) to relevance vector machine(RVM), study of "history" strategy was introduced to FOA, then, FOA with history study ability(HSAFOA) was proposed and effectively improved the classification performance of RVM. Different fault types and different fault degrees of rolling bearing fault diagnosis experiment results show that the LCD symbol entropy can represent fault effectively and HSAFOA improved the identification accuracy of RVM, it has a certain superiority when compared with some other methods.
引文
[1] 雷亚国, 何正嘉, 訾艳阳. 基于混合智能新模型的故障诊断[J]. 机械工程学报, 2008, 44(7): 112-117.LEI Y G, HE ZH J, ZHI Y Y. Fault diagnosis based on novel hybrid intelligent modle[J]. Chinese Journal of Mechanical Engineering, 2008, 44(7): 112-117.
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[4] 张淑清, 孙国秀, 李亮, 等. 基于LMD近似熵和FCM聚类的机械故障诊断研究[J]. 仪器仪表学报, 2013, 34(03): 714-720.ZHANG SH Q, SUN G X, LI L, et al. Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering[J]. Chinese Journal of Scientific Instrument, 2013, 34(3): 714-720.
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[7] 贾峰, 武兵, 熊晓燕, 等. 基于多维度排列熵和支持向量机的轴承早期故障诊断方法[J]. 计算机集成制造系统, 2014, 20(9): 2275-2282.JIA F, WU B, XIONG X Y, et al. Early fault diagnosis of bearing based on multi-dimension permutation entropy and SVM[J]. Computer Integrated Manufacturing Systems, 2014, 20(9): 2275-2282.
[8] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19): 124-131.LI H, ZHANG Q, QIN X R, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neutral network[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131.
[9] 王田田, 王艳, 纪志成. 基于改进极限学习机的滚动轴承故障诊断[J].系统仿真学报,2018, 30(11):4413-4420.WANG T T, WANG Y, JI ZH CH. Fault diagnosis of rolling bearing based on improved extreme learing machine [J].系统仿真学报,2018, 30(11):4413-4420.
[10] 孙瑶琴. 基于改进FOA优化的SVM在故障诊断中的应用[J]. 机械强度, 2017, 39(2): 285-290.SUN Y Q. Optimized SVM based on improved FOA and its application in fault diagnosis[J]. Journal of Mechanical Strength, 2017, 39(2): 285-290.
[11] 吴印华, 徐琼燕. 基于改进蜂群算法优化支持向量机的故障诊断[J]. 机械强度, 2018, 40(2): 287-292.WU Y H, XU Q Y. A fault diagnosis method based on improved artifical bee colony optimize support vector machine[J]. Journal of Mechanical Strength, 2018, 40(2): 287-292.
[12] 陈法法, 刘帅, 肖文荣, 等. 混沌粒子群优化RVM的滚动轴承早期故障诊断[J]. 电子测量与仪器学报, 2018, 32(8): 9-16.CHEN F F, LIU SH, XIAO W R, et al. Roller bearing early fault diagnosis based on relevance vector machine optimized by chaotic particle swarm optimization[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(8): 9-16.
[13] 王波, 刘树林, 蒋超, 等. 基于量子遗传算法优化RVM的滚动轴承智能故障诊断[J]. 振动与冲击, 2015, 34(17): 207-302.WANG B, LIU SH L, JIANG CH, et al. Rolling bearing intelligent fault diagnosis based on RVM optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2015, 34(17): 207-302.
[14] 程军圣, 郑近德, 杨宇. 一种新的非平稳信号分析方法——局部特征尺度分解法[J]. 振动工程学报, 2012, 25(2): 215-220.CHEN J SH, ZHEN J D, YANG Y. A nonstationary signal analysis approach—the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering, 2012, 25(2): 215-220.
[15] PAN W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012(26): 69-74..
[16] 李霞, 孙灵芳, 杨明. 基于改进FOA匹配追踪的超声信号处理研究[J]. 仪器仪表学报, 2013, 34(9): 2068-2073.LI X, SUN L F, YANG M. Research on ultrasonic signal processing based on improved FOA maching pursuit[J]. Chinese Journal of Scientific Instrument, 2013, 34(9): 2068-2073.
[2] 李锋, 王家序, 杨荣松. 有监督不相关正交局部保持映射故障辨识[J]. 仪器仪表学报, 2013, 34(5): 1113-1120.LI F, WANG J X, YANG R S. Fault identification method based on supervised uncorrelated orthogonal locality preserving projection[J]. Chinese Journal of Scientific Instrument, 2013, 34(5): 1113-1120.
[3] 许凡, 方彦军, 张荣, 等. 基于LMD基本尺度熵的AP聚类滚动轴承故障诊断[J]. 计算机应用研究, 2016, 33(7): 1-7.XU F, FANG Y J, ZHANG R, et al. Rolling bearing fault diagnosis method based on LMD base-scale entropy and AP clustering[J]. Application Research of Computers, 2016, 33(7): 1-7.
[4] 张淑清, 孙国秀, 李亮, 等. 基于LMD近似熵和FCM聚类的机械故障诊断研究[J]. 仪器仪表学报, 2013, 34(03): 714-720.ZHANG SH Q, SUN G X, LI L, et al. Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering[J]. Chinese Journal of Scientific Instrument, 2013, 34(3): 714-720.
[5] 向丹, 葛爽. 基于EMD样本熵-LLTSA的故障特征提取方法[J]. 航空动力学报, 2014, 29(7): 1535-1542.XIANG D, GE SH. Method of fault feature extraction based on EMD sample entropy and LLTSA[J]. Journal of Aerospace Power, 2014, 29(7): 1535-1542.
[6] 张前图, 房立清, 赵玉龙, 等. 基于LCD信息熵特征和SVM的机械故障诊断[J]. 机械传动, 2015, 39(12): 144-148.ZHANG Q T, FANG L Q, ZHAO Y L, et al. Mechanical fault diagnosis based on LCD information entropy feature and SVM[J]. Journal of Mechanical Transmission, 2015, 39(12): 144-148.
[7] 贾峰, 武兵, 熊晓燕, 等. 基于多维度排列熵和支持向量机的轴承早期故障诊断方法[J]. 计算机集成制造系统, 2014, 20(9): 2275-2282.JIA F, WU B, XIONG X Y, et al. Early fault diagnosis of bearing based on multi-dimension permutation entropy and SVM[J]. Computer Integrated Manufacturing Systems, 2014, 20(9): 2275-2282.
[8] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19): 124-131.LI H, ZHANG Q, QIN X R, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neutral network[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131.
[9] 王田田, 王艳, 纪志成. 基于改进极限学习机的滚动轴承故障诊断[J].系统仿真学报,2018, 30(11):4413-4420.WANG T T, WANG Y, JI ZH CH. Fault diagnosis of rolling bearing based on improved extreme learing machine [J].系统仿真学报,2018, 30(11):4413-4420.
[10] 孙瑶琴. 基于改进FOA优化的SVM在故障诊断中的应用[J]. 机械强度, 2017, 39(2): 285-290.SUN Y Q. Optimized SVM based on improved FOA and its application in fault diagnosis[J]. Journal of Mechanical Strength, 2017, 39(2): 285-290.
[11] 吴印华, 徐琼燕. 基于改进蜂群算法优化支持向量机的故障诊断[J]. 机械强度, 2018, 40(2): 287-292.WU Y H, XU Q Y. A fault diagnosis method based on improved artifical bee colony optimize support vector machine[J]. Journal of Mechanical Strength, 2018, 40(2): 287-292.
[12] 陈法法, 刘帅, 肖文荣, 等. 混沌粒子群优化RVM的滚动轴承早期故障诊断[J]. 电子测量与仪器学报, 2018, 32(8): 9-16.CHEN F F, LIU SH, XIAO W R, et al. Roller bearing early fault diagnosis based on relevance vector machine optimized by chaotic particle swarm optimization[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(8): 9-16.
[13] 王波, 刘树林, 蒋超, 等. 基于量子遗传算法优化RVM的滚动轴承智能故障诊断[J]. 振动与冲击, 2015, 34(17): 207-302.WANG B, LIU SH L, JIANG CH, et al. Rolling bearing intelligent fault diagnosis based on RVM optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2015, 34(17): 207-302.
[14] 程军圣, 郑近德, 杨宇. 一种新的非平稳信号分析方法——局部特征尺度分解法[J]. 振动工程学报, 2012, 25(2): 215-220.CHEN J SH, ZHEN J D, YANG Y. A nonstationary signal analysis approach—the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering, 2012, 25(2): 215-220.
[15] PAN W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012(26): 69-74..
[16] 李霞, 孙灵芳, 杨明. 基于改进FOA匹配追踪的超声信号处理研究[J]. 仪器仪表学报, 2013, 34(9): 2068-2073.LI X, SUN L F, YANG M. Research on ultrasonic signal processing based on improved FOA maching pursuit[J]. Chinese Journal of Scientific Instrument, 2013, 34(9): 2068-2073.