Rotor state diagnosis based on fuzzy C-mean value clustering and its axial center orbit features

PDF(2533 KB)

Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (15) : 27-35.

WEN Guangrui1,2,3,CHEN Zheng1,2,ZHANG Zhifen1,2

Author information +

History +

Abstract

Aiming at the problem of using existing axial center orbit features to identify rotor fault level having lower recognition accuracy and poor effect, a new feature extraction approach for rotor axial center orbit was proposed based on rotor axial center orbit quadrant information entropy.With this method, rotor axial center orbit was divided into four ranges according to quadrants, the information entropy of each range was calculated, respectively and taken as fault features.Then the fuzzy clustering was applied to do fault pattern recognition and fault level one.Effects of mesh size on clustering effect were analyzed to judge the method for determining key parameters in process to acquire quadrant information entropy.The stability of the fuzzy C-mean value clustering was improved by initializing clustering center.Fault simulation tests with different patterns and different levels were performed on a test platform.New indexes proposed here were compared with existing rotor axial center orbit features.The results showed that the proposed approach has a remarkable performance in recognition effect and data visualization, and can provide a new idea for further real time state monitoring and fault accurate diagnosis.

Key words

Rotor / Axis Orbit / Information Quadrants Entropy / Fuzzy C-Means Clustering

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WEN Guangrui1,2,3,CHEN Zheng1,2,ZHANG Zhifen1,2. Rotor state diagnosis based on fuzzy C-mean value clustering and its axial center orbit features[J]. Journal of Vibration and Shock, 2019, 38(15): 27-35

References

[1] 刘雄.屈梁生转子监测和诊断系统[M]. 西安交通大学出版社, 1991.
Liu Xiong，Qu Liangsheng,Rotor monitoring and diagnosis system[M].Xi'an Jiaotong University Press,1991.
[2] 王清. 基于模糊聚类和灰色理论的齿轮箱故障诊断研究[D]. 太原:中北大学, 2006.
Qing Wang. Research on the Fuzzy Glustering and the Grey Theory in the Fault Diagnosid of the Gearbox.[D]TaiYuan : North University of China.2006.
[3] 耿俊豹, 黄树红, 金家善,等. 基于信息熵贴近度和证据理论的旋转机械故障诊断方法[J]. 机械科学与技术, 2006, 25(6):663-666.
Geng J B, Huang S H, Jin J S, et al. A Rotational Machinery Fault Diagnosis Method Based on Close Degree of Information Entropy and Evidence Theory[J]. Mechanical Science & Technology, 2006. 25(6):663-666.
[4] 赵冲冲, 廖明夫, 于潇. 基于支持向量机的旋转机械故障诊断[J]. 振动、测试与诊断, 2006, 26(1):53-57.
Zhao C C，Liao M F，Yu X Application of Support Vecter Machine to Fault Diagnosis of Rotation Machinery[J]. Journal of Vibration Measurement & Diagnosis, 2006.26(1):53-57.
[5] Chen C S, Chen J S. Rotor fault diagnosis system based on sGA-based individual neural networks[J]. Expert Systems with Applications, 2011, 38(9):10822-10830.
[6] 李海波. 大型风力发电机齿轮箱故障模糊诊断技术研究[D].沈阳市：沈阳工业大学, 2014.
Li Haibo. Research on Fuzzy Diagnostic Techniques for Gearbox's Faults of Megawatt Wind Generator[D], ShenYang: Shenyang University of Technology,2014.
[7] Cheng J, Yu D, Tang J, et al. Local rub-impact fault diagnosis of the rotor systems based on EMD[J]. Mechanism & Machine Theory, 2009, 44(4):784-791.
[8] Yang Y, Ren X, Qin W. Nonlinear Response Prediction of Cracked Rotor Based On EMD[C]// Aiaa/asme/asce/ahs/asc Structures, Structural Dynamics, and Materials Conference. 2013.
[9] 李兵, 张培林, 任国全,等. 基于数学形态学的分形维数计算及在轴承故障诊断中的应用[J]. 振动与冲击, 2010, 29(5):191-194.
Li B , Zhang P L, Ren G Q et al. Mathematic morphology-based fractal dimension calculation and its application in fault diagnosis of roller bearings[J]. Journal of Vibration & Shock, 2010, 29(5):191-194.
[10] 曾求洪, 宾光富, 李学军,等. 基于小波包近似熵与LMS加权特征融合异步电机故障诊断[J]. 噪声与振动控制, 2015, 35(5):139-144.
Zeng Q H, Bing G F, Li X J, Asynchronous Motor Fault Diagnosis Based on Wavelet Packet Approximate Entropy and Weighted LMS[J]. NOISE AND VIBRATION CONTROL. 2015, 35(5):139-144.
[11] Lu N, Xiao Z, Malik O P. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery[J]. Mechanical Systems & Signal Processing, 2015, s 52–53(1):393-415.
[12] 张华, 曾文韬, 鄢威. 基于符号动力学信息熵与SVM的液压泵故障诊断[J]. 振动、测试与诊断, 2017, 37(2):288-293.
Zhang H, Zeng W T, Yan W. Fault Diagnosis of Hydraulic Pump Based on Symbolic Dynamics Entropy and SVM[J]. Zhendong Ceshi Yu Zhenduan/journal of Vibration Measurement & Diagnosis, 2017, 37(2):288-293.
[13] Imaouchen Y, Kedadouche M, Alkama R, et al. A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection[J]. Mechanical Systems & Signal Processing, 2016, 82(1):103-116.
[14] Peng Y, Cheng J, Yang Y, et al. Adaptive sparsest narrow-band decomposition method and its applications to rotor fault diagnosis[J]. Measurement, 2016, 91(9):451-459.
[15] Fu B, Zhou J Z, Chen W Q, et al. Identification of the shaft orbits for turbine rotor by modified Fourier descriptors[C]// International Conference on Machine Learning and Cybernetics. IEEE, 2004:1162-1167 vol.2.
[16] 倪传坤, 周建中, 付波. 基于改进不变矩的发电机组轴心轨迹识别[J]. 电力科学与工程, 2004(2):16-18.
Chuankun N I, Zhou J, Bo F U. Automatic Identification of Shaft Orbits of Generator Sets Based on Improved Moment Invariant[J]. Electric Power Science & Engineering, 2004.
[17] Chen X, Zhou J, Xiao H, et al. Fault diagnosis based on comprehensive geometric characteristic and probability neural network[J]. Applied Mathematics & Computation, 2014, 230(3):542-554.
[18] 王红军, 徐小力, 万鹏. 基于轴心轨迹流形拓扑空间的转子系统故障诊断[J]. 机械工程学报, 2014, 50(5):95-101.
Wang H. Rotor System Fault Diagnosis Based on Orbit Manifold Topological[J]. Journal of Mechanical Engineering, 2014, 50(5):95.
[19] 王朝晖, 张来斌, 刘玉辉. 柴油机滑动油承磨损状况的轴心轨迹诊断法[J]. 石油矿场机械, 2002, 31(3):6-8.
Wang Z, Zhang L, Liu Y. The axes track diagnostic method for diesel engine′s plain bearing wearing condition[J]. Oil Field Equipment, 2002.
[20] 王朝晖, 张来斌, 万波. 基于轴心轨迹的滑动轴承运行稳定性综合评价方法[J]. 煤炭学报, 2007, 32(12):1328-1333.
Wang Z H, Zhang L B, Wan B. Comprehensive evaluation of sliding bearing operation stability based on axle centre trail[J]. Journal of China Coal Society, 2007.
[21] 胥永刚, 何正嘉. 基于二维近似熵度量轴心轨迹复杂性的研究[J]. 西安交通大学学报, 2003, 37(11):1171-1174.
Xu Y, He Z. Research on Complexity Measure of Axis Orbit Based on Two-Dimensional Approximate Entropy[J]. Journal of Xian Jiaotong University, 2003, 37(11):1171-1174.
[22] 蒋林, 高德远, 谢晓燕. 转子运行稳定性--大型回转机械瞬时运行状态评价[J]. 西北工业大学学报, 2000, 18(3):457-461.
Jiang L, Gao D. Rotating Stability——A New Criterion for Evaluating Instantaneous Running Condition of Large Machinery[J]. Journal of Northwestern Polytechnical University, 2000.
[23] 胡道达, 马振利, 石文凯. 基于分形盒维数的油膜涡动与油膜振荡轴心轨迹分析[J]. 重庆理工大学学报, 2015, 29(11):78-82.
Axis Track Analysis on Oil Whirl and Oil Oscillation Based on Fractal Box Counting Dimension
[24] 蒋东翔, 黄文虎, 徐世昌,等. 旋转机械运行状态的模糊模式识别[J]. 哈尔滨工业大学学报, 1995，27(1):89-93.
Jiang D, Huang W H, Xu S C, et al. Fuzzy Pattern Recognition for Rotating Machinery Condition[J]. Journal of Harbin Institute of Technology, 1995, 27(1):89-93
[25] Bezdek J C, Ehrlich R, Full W. FCM: The fuzzy c -means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2):191-203.
[26] 朱林, 王士同, 邓赵红. 改进模糊划分的FCM聚类算法的一般化研究[J]. 计算机研究与发展, 2009, 46(5):814-822.
Zhu L, Chung F L, Wang S. Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions.[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2009, 46(5):814-822.
[27] Hathaway R J, Bezdek J C. Fuzzy c-means clustering of incomplete data[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2002, 31(5):735-744.