一种基于复合谱与关联熵融合的特征提取方法

doi:10.3901/JME.2017.24.096

机械工程学报 ›› 2017, Vol. 53 ›› Issue (24): 96-103.doi: 10.3901/JME.2017.24.096

扫码分享

一种基于复合谱与关联熵融合的特征提取方法

孙健¹, 李洪儒²

1. 中国洛阳电子装备试验中心洛阳 471003;
2. 陆军工程大学石家庄校区石家庄 050003

收稿日期:2016-10-15 修回日期:2017-07-20 发布日期:2017-12-20
通讯作者: 李洪儒(通信作者),男,1961年出生,博士,教授,博士研究生导师。主要研究方向为装备状态监测与故障预测。E-mail:lihr168@sohu,com
作者简介:孙健,男,1987年出生,博士。工程师。主要研究方向为信号处理与分析。E-mail:hehetcs@163.com
基金资助:
国家自然科学基金资助项目（51275524）。

Method for Feature Extraction Based on Composite Spectrum and Relative Entropy Fusion

SUN Jian¹, LI Hongru²

1. Luoyang Electronic Equipment Test Center of China, Luoyang 471003;
2. The Shijiazhuang Branch of The Army Engineering University, Shijiazhuang 050003

Received:2016-10-15 Revised:2017-07-20 Published:2017-12-20

摘要/Abstract

摘要： 液压泵特征提取是实现故障预测的关键环节。针对液压泵退化特征不理想的问题，提出一种基于改进复合谱与关联熵融合的特征提取方法。首先，对传统CS算法进行改进，对多通道振动信号进行融合，实现对特征信息的综合利用，并分别提取Shannon定义下和Tsallis定义下的DCS功率谱熵和DCS奇异熵作为特征；在此基础上，提出基于关联熵的融合方法，将所提取的特征融合为一个全新特征，作为液压泵退化特征，提高特征的简洁度；最后，利用液压泵性能退化试验所采集振动信号，验证了该方法的有效性。

关键词: 复合谱, 退化特征提取, 信息融合, 信息熵

Abstract: An feature extraction of the key step in prognostic of hydraulic pump. Since vibration signals of hydraulic pump are complex and degradation features are hard to extract, a novel method based upon DCS and relation entropy is proposed. First of all, in order to make reasonable use of feature information, earlier CS is modified by DCT and the DCS algorithm is presented to make fusion of multi-channel vibration signals. And DCS power entropy and singular entropy, which are relatively defined in Shannon entropy and Tsallis entropy, are extracted as features. On this basement, the feature fusion method based on relation entropy is proposed to remain original features performances and improve conciseness. According to max relation entropy criterion and gradual fusion strategy, the four extracted features are fused into a new one, which is considered as degradation feature. Finally, the proposed method is verified by vibration signals sampled from hydraulic pump degradation experiment.

Key words: composite spectrum, degradation feature extraction, information fusion, relative entropy

中图分类号:

TH212
TH213

孙健, 李洪儒. 一种基于复合谱与关联熵融合的特征提取方法[J]. 机械工程学报, 2017, 53(24): 96-103.

SUN Jian, LI Hongru. Method for Feature Extraction Based on Composite Spectrum and Relative Entropy Fusion[J]. Journal of Mechanical Engineering, 2017, 53(24): 96-103.

参考文献

[1] DU Jun, WANG Shaoping, ZHANG Haiyan. Layered clustering multi-fault diagnosis for hydraulic piston pump[J]. Mechanical Systems and Signal Processing, 2013, 36(2):487-504.
[2] SUN Jian, LI Hongru, XU Baohua. The morphological undecimated wavelet decomposition-discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps[J]. Measurement, 2016, 94:794-805.
[3] SUN Jian, LI Hongru, XU Baohua. Degradation feature extraction of the hydraulic pump based on high-frequency harmonic local characteristic-scale decomposition sub-signal separation and discrete cosine transform high-order singular entropy[J]. Advances in Mechanical Engineering, 2016, 8(7):1-12.
[4] 许治, 戴宁, 张长龙, 等. 基于迭代变形的多源数据融合技术[J]. 机械工程学报, 2014, 50(7):191-198. XU Zhi, DAI Ning, ZHANG Changlong, et al. Multi-sensor data fusion based on iterative deformation[J]. Journal of Mechanical Engineering, 2014, 50(7):191-198.
[5] CIUONZO D, PAPA G, ROMANO G, et al. One-bit decentralized detection with a rao test for multi-sensor fusion[J]. IEEE Signal Processing Letters, 2013, 20(9):861-864.
[6] SAFIZADEH M S, LATIFI S K. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell[J]. Information fusion, 2014, 18:1-8.
[7] ANGELOV P, YAGER R. Density-based averaging-A new operator for data fusion[J]. Information Science, 2013, 222:164-174.
[8] WEI C M, BLUM R S. Theoretical analysis of correlation-based quality measures for weighted averaging image fusion[J]. Information Fusion, 2010, 11:301-309.
[9] RODGER J A. Toward reducing failure risk in an integrated vehicle health maintenance system:A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS[J]. Expert Systems with Applications, 2012, 39:9821-9835.
[10] YANG W B, LI S Y. A switch-mode information fusion filter based on ISRUKF for autonomous navigation of spacecraft[J]. Information Fusion, 2014, 18:33-42.
[11] REN Y F, KE X Z. Multi-wavelet-basis multi-scale multi-sensor data fusion[J]. Transducer and Micro-system Technologies, 2010, 29(9):77-79.
[12] KERI E, SINHA J K. Vibration-based condition monitoring of rotating machines using a machine composite spectrum[J]. Journal of Sound and Vibration, 2013, 332:2831-2845.
[13] AKILU Y K, SINHA J K, KERI E. An improved data fusion technique for faults diagnosis in rotating machines[J]. Measurement, 2014, 58:27-32.
[14] HUANG Hai, XIAO Liyi, LIU Jiaming. CORDIC-based unified architectures for computation of DCT/IDCT/DST/IDST[J]. Circuits, Systems, and Signal Processing, 2014, 33(3):799-814.
[15] YANG Enhui, YU Xiang, MENG Jin, et al. Transparent composite model for DCT coefficients:design and analysis[J]. IEEE Transactions on Image Processing, 2014, 23(3):1303-1316
[16] CHEN Wensheng, DAI Xiul, PAN Binbin, et al. A novel discriminate criterion based on feature fusion strategy for face recognition[J]. Neurocomputering, 2015,159:67-77.
[17] JAEIK J, SUNG J L, KANG R P, et al. Detecting driver drowsiness using feature-level fusion and user-specific classification[J]. Expert Systems with application, 2014, 41:1139-1152.
[18] 邬再新, 刘涛, 黄东成. 基于信息熵的涡旋压缩机振动信号分析[J]. 振动、测试与诊断. 2014, 34(1):168-172. WU Zaixin, LIU Tao, HUANG Dongcheng. Analysis of vibration signals of scroll compressor based on information entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2014, 34(1):168-172.
[19] FIORI E R, PLASTINO A. A Shannon-Tsallis transformation[J]. Physica A:Statistical Mechanics and Its Application, 2013,392(8):1742-1749.
[20] PETRE C. The predictive power of singular value decomposition entropy for stock market dynamics[J]. Physica A:Statistical Mechanics and Its Application, 2014, 393:571-578.
[21] PENG Weimin, DENG Huifang. Quantum inspired method of feature fusion based on von Neumann entropy[J]. Information Fusion, 2014, 18:9-19.
[22] 李怀俊, 谢小鹏. 基于核特征模糊聚类及模糊关联熵的齿轮故障模式识别[J]. 仪器仪表学报, 2015, 36(4):849-854. LI Huaijun, XIE Xiaopeng. Gear fault pattern recognition based on kernel feature fuzzy clustering and fuzzy association entropy[J]. Chinese Journal of Scientific Instrument, 2015, 36(4):849-854.
[23] ZARINBAL M, ZARANDI M H F, THRKSEN I B. Relative entropy fuzzy c-means clustering[J]. Information Science, 2014, 260:74-97.
[24] 程哲. 直升机传动系统行星轮系损伤建模与故障预测理论及方法研究[D]. 长沙:国防科学技术大学, 2011. CHENG Zhe. Theory and method on damage modeling and prognostics for planetary gear set of helicopter transmission system[D]. Changsha:National University of Defense Technology, 2011.

一种基于复合谱与关联熵融合的特征提取方法

Method for Feature Extraction Based on Composite Spectrum and Relative Entropy Fusion

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	钱敏, 耿可可, 殷国栋, 李尚杰, 王子威, 孙宇啸. 基于多元信息融合的车路协同重识别算法[J]. 机械工程学报, 2024, 60(16): 231-240.
[2]	肖春昱, 罗湘萍, 田师峤, 宫岛. 基于转向架姿态轨迹的轨道线路曲率探测系统研究[J]. 机械工程学报, 2023, 59(6): 204-213.
[3]	滕洪钊, 邓朝晖, 吕黎曙, 谷倩微, 刘涛, 卓荣锦. 多传感器信息融合的加工过程状态监测研究[J]. 机械工程学报, 2022, 58(6): 26-41.
[4]	赵杰, 武睿, 张赫, 朱延河, 臧希喆. 面向复杂力交互任务的操作技能传递与控制研究[J]. 机械工程学报, 2022, 58(18): 116-132.
[5]	申慧敏, 葛瑞康, 顾晓伟, 蔡晓童, 甘屹, 杨赓. 基于磁-惯性传感融合的灵巧手指单元全姿态信息反演方法[J]. 机械工程学报, 2022, 58(18): 133-140.
[6]	张朝龙, 赵筛筛, 何怡刚. 基于信息熵与PSO-LSTM的锂电池组健康状态估计方法[J]. 机械工程学报, 2022, 58(10): 180-190.
[7]	任彦君, 殷国栋, 沙文瀚, 沈童. 基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法[J]. 机械工程学报, 2021, 57(8): 184-194.
[8]	姜武华, 辛鑫, 陈无畏, 崔卫卫. 基于信息融合的自动泊车系统多工况车位识别和决策规划[J]. 机械工程学报, 2021, 57(6): 131-141.
[9]	叶壮, 余建波. 基于多通道加权卷积神经网络的齿轮箱振动信号特征提取[J]. 机械工程学报, 2021, 57(1): 110-120.
[10]	熊璐, 金达, 冷搏, 余卓平, 杨兴. 考虑复杂激励条件的分布式驱动电动汽车路面附着系数自适应估计方法[J]. 机械工程学报, 2020, 56(18): 123-133.
[11]	薛培林, 吴愿, 殷国栋, 刘帅鹏, 林乙蘅, 黄文涵, 张云. 基于信息融合的城市自主车辆实时目标识别[J]. 机械工程学报, 2020, 56(12): 165-173.
[12]	常树鹤, 张昊宇, 许海鹰, 洪宇翔, 王力, 都东. 基于视觉信息融合的熔丝成形制造路径在线识别[J]. 机械工程学报, 2019, 55(17): 1-7.
[13]	张袅娜, 郭孔辉, 丁海涛. 基于多源信息融合的行驶工况识别及其在整车转矩分配中的应用[J]. 机械工程学报, 2017, 53(24): 135-143.
[14]	张明, 江志农. 基于多源信息融合的往复式压缩机故障诊断方法[J]. 机械工程学报, 2017, 53(23): 46-52.
[15]	江浩斌, 沈峥楠, 马世典, 陈龙. 基于信息融合的自动泊车系统车位智能识别[J]. 机械工程学报, 2017, 53(22): 125-133.