Fault diagnosis of steam turbine rotor based on permutation entropy and IFOA-RVM

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (5) : 79-84.

SHI Zhi-biao1 CHEN Fei1 CAO Li-hua2

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Abstract

In order to improve the accuracy and efficiency of steam turbine rotor fault diagnosis，a fault diagnosis method for steam turbine rotor was proposed based on permutation entropy and relevance vector machine (RVM) optimized by improved fruit fly optimization algorithm (IFOA). The experimental data were decomposed with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The permutation entropy was calculated with IMF components being sensitive to fault features, it was used to construct the feature sample set. Then the "two fork tree" IFOA-RVM fault classifier was established to classify the feature set. IFOA was used to define two-stage fruit fly population search ranges to improve the search efficiency, meanwhile RVM kernel function was avoided to fall into local optimum. The fault data obtained on the ZT-3 steam turbine rotor analog test rig were studied. The results showed that compared with the fuzzy entropy, the clustering effect of the feature sample set obtained with permutation entropy is obvious; IFOA-RVM classifier is superior to FOA-RVM classifier in fault classification accuracy and efficiency; the validity and feasibility of the fault diagnosis method for steam turbine rotor based on permutation entropy and IFOA-RVM were verified.

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IFOA / RVM / steam turbine rotor / fault diagnosis

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SHI Zhi-biao1 CHEN Fei1 CAO Li-hua2. Fault diagnosis of steam turbine rotor based on permutation entropy and IFOA-RVM[J]. Journal of Vibration and Shock, 2018, 37(5): 79-84

References

[1] 杨宇，王欢欢，喻镇涛，等. 基于ITD改进算法和关联维数的转子故障诊断方法[J]. 振动与冲击，2012, 31(23): 67-70.
YANG Yu, WANG Huan-huan, YU Zhen-tao,etal. A rotor fault diagnosis method based on ITD improved algorithm and correlation dimension [J]. Journal of Vibration and Shock, 2012, 31(23): 67-70.
[2] 石志标，苗莹. 基于FOA-SVM的汽轮机振动故障诊断[J]. 振动与冲击，2014, 33(22): 111-114.
SHI Zhi-biao, MIAO Ying. Vibration fault diagnosis for steam turbine by using support vector machine based on fruit fly optimization algorithm [J]. Journal of Vibration and Shock, 2014, 33(22): 111-114.
[3] 陈向民，于德介，李蓉. 基于信号共振稀疏分解与重分配小波尺度谱的转子碰摩故障诊断方法[J]. 振动与冲击， 2013, 32(13):27-33.
CHEN Xiang-min, YU De-jie, LI Rong. Rub-impact diagnosis of rotors with resonance-based sparse signal decomposition and reassigned wavelet scalogram [J]. Journal of Vibration and Shock, 2013, 32(13): 27-33.
[4] Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series [J]. Physical Review Letters, 2002, 88(17): 174102(1-4).
[5] 王立昆. 基于RVM的手写体数字识别[D]. 西安：西安电子科技大学，2011.
WANG Li-kun. Handwritten Digit Recognition Based on RVM[D]. Xi An: Xidian University, 2011.
[6] 段青，赵建国，马艳. 优化组合核函数相关向量机电力负荷预测模型[J]. 电机与控制学报， 2010, 14(6): 33-38.
DUAN Qing, ZHAO Jian-guo, MA Yan. Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting[J]. Electric Machines and Control, 2010, 14(6) :33-38.
[7] 易辉，梅磊，李丽娟，等. 基于多分类相关向量机的水电机组振动故障诊断[J]. 中国电机工程学报，2014, 34(17): 2843-2850.
YI Hui, MEI Lei, LI Lijuan,etal. Vibration Fault Diagnosis for Hydroelectric Generating Units Using the Multi-class Relevance[J]. Proceedings of the CSEE, 2014, 34(17): 2843-2850.
[8] 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): 207-212.
WANG Bo, LIU Shu-lin, JIANG Chao,etal.Rolling bearings' intelligent fault diagnosis based on RVM optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2015, 34(17): 207-212.
[9] 姚畅，陈后金，Yang Yong-Yi，等. 基于自适应核学习相关向量机的乳腺X线图像微钙化点簇处理方法研究[J]. 物理学报，2013, 62(8): 088702(1-11).
Yao Chang, Chen Hou-Jin, Yang Yong-Yi,etal. Microcalcification clusters processing in mammograms based on relevance vector machine with adaptive kernel learning[J]. Acta Physica Sinica, 2013, 62(8): 088702(1-11).
[10] Lima C A M, Coelho A L V, Chagas S. Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines[J]. Expert Systems with Applications, 2009, 36(6): 10054-10059.
[11] 金理钻，屠珺，刘成良. 基于迭代式RELIEF和相关向量机的黄瓜图像识别方法[J]. 上海交通大学学报，2013, 47(4): 602-606
JIN Li-zuan，TU Jun，LIU Cheng-liang. A Method for cucumber Indentification Based on Iterative-RELIEF and Relevance Vector Machine[J]. Journal of Shanghai Jiaotong University, 2013, 47(4): 602-606
[12] 柳长源. 相关向量机多分类算法的研究与应用[D]. 哈尔滨：哈尔滨工程大学，2013.
LIU Chang-yuan. Research and Application on the Multi-
classification of Relevance Vector Machine Algorithm[D]. Harbin: Harbin Engineering University, 2013.
[13] Tipping M E.The Relevance Vector Machine[J]. Neural Networks & Machine Learning, 1999, 1(3): 652-658.
[14] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine[J]. Journal Of Machine Learning Research, 2001, 1(3): 211-244.
[15] Pan W T. A new fruit fly optimization algorithm: taking the financial distress model as an example [J]. Knowledge-Based Systems, 2012, 26(2): 69 -74.
[16] 徐富强，陶有田，吕洪升. 一种改进的果蝇优化算法[J]. 苏州大学学报：自然科学版，2014, 29(1): 16-23.
Xu Fuqiang, Tao Youtian, Lyu Hongsheng. Improved fruit fly optimization algorithm[J] Journal of Soochow University : Natural Siencei Edition, 2014, 29(1): 16-23.
[17] 徐自励，王一扬，周激流. 估计非线性时间序列嵌入延迟时间和延迟时间窗的C-C平均方法[J]. 四川大学学报：工程科学版，2007, 39(1): 151-155.
XU Zi-li, WANG Yi-yang, ZHOU Ji-liu. C-C Average Method for Estimating the Delay Time and the Delay Time Window of Nonlinear Time Series Embedding[J]. Journal of Sichuan University : Engineering Science Edition, 2007, 39(1): 151-155.
[18] Walton J, Fairley N. Noise Reduction in X-ray Photoelectron Spectromicroscopy by A Singular Value Decomposition Sorting Procedure[J]. Journal of Electron Spectroscopy and Related Phenomena, 2005, 148(1): 29-40.
[19] 侯平魁，龚云帆，杨毓英，等.水下目标辐射噪声时间序列的非线性降噪处理[J].声学学报，2001, 26(3): 207-211.
HOU Ping-kui, GONG Yun-fan, YANG Yu-ying,etal. Nonlinear noise reduction of the underwater target radiated noise time series[J]. Acta Acustica, 2001, 26(3): 207-211.
[20] Colominas M A, Schlotthauer G, Torres M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing & Control, 2014, 14(1): 19-29.
[21] 王波，刘树林，张宏利，等. 相关向量机及其在机械故障诊断中的应用研究进展[J]. 振动与冲击，2015, 34(5): 145-153.
WANG Bo, LIU Shu-lin, ZHANG Hong-li,etal. Advances about relevance vector machine and its applications in machine fault diagnosis[J]. Journal of Vibration and Shock, 2015, 34(5): 145-153.