Time-domain Compression Feature Extraction and Application Study of Compressed Sensing in Equipment Status Assessment
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摘要: 压缩感知是一种新的信号采集与处理框架,其框架中压缩采样过程能够直接获取"压缩"的采样数据。本文中研究了如何利用这些压缩数据提取特征并用于设备的状态评估。首先在压缩感知框架下研究压缩采样数据的特点,研究压缩数据的压缩性与信号的稀疏性的对应关系;接着提出一种时域压缩特征计算方法,用于提取压缩数据的特征信息;最后以滚动轴承为对象,使用时域压缩特征对滚动轴承的运行状态进行评估。使用滚动轴承全寿命周期数据进行实验分析,实验结果表明,时域压缩特征能够准确的判断轴承的运行状态。Abstract: Compressed sensing is a new theory of signal acquisition and processing. Base on this theory, compressed data is acquired in the process of compression sampling. In this paper, it is studied that which features is extracted from the compressed data and how these features used for equipment state estimation. Firstly, compressed data is analyzed and the study tried to find the corresponding relationship between compression data and signal sparsity. A time-domain compression characteristics is proposed, and used to extracting characteristic information hidden in the compressed data. Rolling bearing is chosen and time-domain characteristic is used to estimate the operational conditions of the rolling bearing. The method is used to analyze the whole life data of rolling bearing. The results show that the time domain compression feature can accurately estimate the running state of rolling bearings, demonstrate the effectiveness of the proposed method.
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Key words:
- compressed sensing /
- rolling bearing /
- feature extraction /
- state estimation
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[1] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306 [2] Candès E J. Compressive sampling[C]//Proceedings of the International Congress of Mathematicians. Madrid, Spain:European Mathematical Society, 2006 [3] Yang J B, Liao X J, Yuan X, et al. Compressive sensing by learning a Gaussian mixture model from measurements[J]. IEEE Transactions on Image Processing, 2015,24(1):106-119 [4] Krahmer F, Needell D, Ward R. Compressive sensing with redundant dictionaries and structured measurements[J]. arXiv preprint arXiv:1501.03208, 2015 [5] Tiwari V, Bansod P P, Kumar A. Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images[J]. Cogent Engineering, 2015,2(1):1017244 [6] Yuan X, Jiang H, Huang G, et al. Compressive sensing via low-rank gaussian mixture models[J]. arXiv preprint arXiv:1508.06901, 2015 [7] Knarr S H, Howland G, Schneeloch J, et al. Using double compressive sensing in simultaneous imaging of spatial entanglement[C]//CLEO:QELS_Fundamental Science. San Jose, California United States:Optical Society of America, 2015:FF2A.7 [8] Huang B C, Xu K, Wan J W, et al. Distributed compressive sensing of hyperspectral images using low rank and structure similarity property[J]. Sensing and Imaging, 2015,16(1) [9] Ji S, Li L, Huang L P, et al. Compressive sensing method for damage detection in wireless structural health monitoring[C]//Proceedings of the 2nd International Conference of Structural Health Monitoring and Integrity Management, Nanjing, China, 24-26 September 2014. London:CRC Press, 2015:145-148 [10] 刘畅,伍星,毛剑琳,等.基于压缩感知的滚动轴承振动信号压缩方法[J].昆明理工大学学报(自然科学版),2015,40(4):46-50 Liu C, Wu X, Mao J L, et al. Rolling bearing signal compression using compressive sensing[J]. Journal of Kunming University of Science and Technology (Natural Science Edition), 2015,40(4):46-50(in Chinese) [11] 郑晓慧.机械振动信号的稀疏分解理论研究[D]. 兰州:兰州理工大学,2014 Zheng X H. The research on sparse decomposition theory of mechanical vibration signal[D]. Lanzhou:Lanzhou University of Technology, 2014(in Chinese) [12] 刘海宁.基于稀疏编码的设备状态识别及其重型轧辊磨床监测应用[D].上海:上海交通大学,2011 Liu H N. Sparse coding based machine condition recognition and its application in the condition monitoring of a heavy roller grinder[D]. Shanghai:Shanghai Jiaotong University, 2011(in Chinese) [13] 杨国安.滚动轴承故障诊断实用技术[M].北京:中国石化出版社,2012 Yang G A. Bearing fault diagnosis practical technology[M]. Beijing:China Petrochemical Press, 2012(in Chinese) [14] Candès E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008,346(9-10):589-592 [15] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM Journal on Scientific Computing, 1998,20(1):33-61 [16] Foucart S, Rauhut H. A mathematical introduction to compressive sensing[M]. Basel:Birkhäuser, 2013 [17] Lee J, Qiu H, Yu G, et al. Bearing data set[EB/OL]. Moffett Field, CA:IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, NASA Ames Research Center
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