|
|
- 2015
采用概率主成分分析的回转支承寿命状态识别
|
Abstract:
针对回转支承故障特征微弱以及难以提取的特点,提出一种基于概率主成分分析(probabilistic principal component analysis, PPCA)的多领域特征提取方法。该方法从振动信号的时域和时频域中提取出多个能够表征回转支承运行状态的特征向量,并将其组成高维特征集。采用PPCA从高维特征集中提取出最能够反映回转支承寿命状态信息的特征量,将其输入粒子群算法优化的支持向量机中进行寿命状态的识别。通过回转支承全寿命实验证明,基于PPCA的特征提取方法优于传统的主成分分析(principal component analysis, PCA),其相应的寿命状态识别精度提高了约8％,并且多领域、多变量的特征更能全面反映回转支承的性能退化趋势。与传统的特征提取方法相比,所提方法能够更全面有效地反映复杂恶劣环境下回转支承的故障信息,因此可以用于回转支承的健康监测领域。
A novel multi？？domain feature extraction approach based on probabilistic principal component analysis (PPCA) is proposed to deal with the weak fault feature of slewing bearings. Several feature vectors are extracted to form a feature set with high dimension. Then the vectors that best reflect the slewing bearing life status are extracted from the feature set by applying PPCA. These vectors are then used as inputs of a support vector machine with particle swarm optimization to perform the life state recognition. It follows from the whole life experiment of slewing bearing that PPCA is better than the traditional PCA in reducing feature dimension, and its recognition accuracy of lifetime state increases by about 8%. A comparison with a single feature or single domain features shows that the multi？？domain and multi？？feature set reflects the degradation of slewing bearings more comprehensively and accurately. And a comparison with the traditional feature？？extraction method shows that the proposed method reflects the fault of the slewing bearing that is running in a complex and harsh environment more effectively, thus, it can be applied in the area of slewing bearing health monitoring
| [1] | ［1］KOSASIH B Y, CAESARENDRA W, TIEU K, et al. Degradation trend estimation and prognosis of large low speed slewing bearing lifetime ［J］. Applied Mechanics and Materials, 2014, 493: 343？？348. |
| [2] | ［4］DYBALA J, ZIMROZ R. Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal ［J］. Applied Acoustics, 2014, 77(3): 195？？203. |
| [3] | ［12］ZHONG Guosheng, LI Jiang, ZHAO Kui. Structural safety criteria for blasting vibration based on wavelet packet energy spectra ［J］. Mining Science and Technology, 2011, 21(1): 35？？40. |
| [4] | ［5］KUMAR H S, SRINIVASA P P, SRIRAM N S, et al. ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing ［J］. Procedia Engineering, 2013, 20(2013): 805？？814. |
| [5] | ［6］HONG Sheng, ZHOU Zheng, ZIO E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method ［J］. Digital Signal Processing, 2014, 27(2): 159？？166. |
| [6] | ZHANG Meijun, WANG Chuang, CHEN Hao. The application research on the combination of IMF energy and RBF neural network in rolling bearing ［J］. Machinery, 2012, 39(6): 63？？70. |
| [7] | ［14］DE MOURA E P, SOUTO C R, SILVA A A, et al. Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses ［J］. Mechanical Systems and Signal Processing, 2011, 25(5): 1765？？1772. |
| [8] | ［15］YU Lingbo, SNAPP R R, RUIZ T, et al. Probabilistic principal component analysis with expectation maximization(PPCA？？EM) facilitates volume classification and estimates the missing data ［J］. Journal of Structural Biology, 2010, 171(1): 18？？30. |
| [9] | ［2］YANG Yu, YU Dejie, CHENG Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN ［J］. Journal of Sound and Vibration, 2006, 294(1/2): 269？？277. |
| [10] | ［3］张梅军, 韩思晨, 石文磊, 等. IMF分量的倒频谱分析在滚动轴承故障诊断中的应用 ［J］. 液压气动与密封, 2012, 32(3): 33？？35. |
| [11] | ZHANG Meijun, HAN Sichen, SHI Wenlei, et al. Cepstrum analysis of the IMF components in ball bearing fault diagnosis ［J］. Hydraulics Pneumatics and Seals, 2012, 32(3): 33？？35. |
| [12] | ［7］DONG Shaojiang, LUO Tianhong. Bearing degradation process prediction based on the PCA and optimized LS？？SVM model ［J］. Measurement, 2013, 46(9): 3143？？3152. |
| [13] | ［8］刘永前, 徐强, 田德, 等. 基于萤火虫神经网络的轴承性能退化程度评估 ［J］. 机械传动, 2014, 38(5): 107？？109. |
| [14] | LIU Yongqian, XU Qiang, TIAN De, et al. Bearing performance degradation assessment using optimized BP neural network based on glowworm swarm optimization algorithm ［J］. Journal of Mechanical Transmission, 2014, 38(5): 107？？109. |
| [15] | ［9］CAESARENDRA W, KOSASIH B, TIEU A K, et al. Circular domain features based condition monitoring for low speed slewing bearing ［J］. Mechanical Systems and Signal Processing, 2014, 45(1): 114？？138. ［10］ZVOKELJ M, ZUPAN S, PREBIL I. Multivariate and multiscale monitoring of large？？size low？？speed bearings using ensemble empirical mode decomposition method combined with principal component analysis ［J］. Mechanical Systems and Signal Processing, 2010, 24(4): 1049？？1067. |
| [16] | ［13］张梅军, 王闯, 陈灏. IMF能量和RBF神经网络相结合在滚动轴承故障诊断中的应用研究 ［J］. 机械, 2012, 39(6): 63？？70. |
| [17] | ［11］ZVOKELJ M, ZUPAN S, PREBIL I. Non？？linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method ［J］. Mechanical Systems and Signal Processing, 2011, 25(7): 2631？？2653. |
