Fault Diagnosis Method for Rotating Machinery Based on AMD

Abstract

Abstract:

Aiming at the problems of fault diagnosis for rotating machinery, a fault diagnosis method for rotating machinery was proposed based on AMD herein. As long as the frequency components of signals were known, signals with different frequency components might be decomposed into single frequency signals using the AMD method, especially to decompose a signal with closely spaced frequency components. For the fault feature frequency prediction in rotating machinery fault diagnosis, AMD method might be used to extract fault feature frequency signals in mechanical vibration signals and the frequency spectrum was obtained. If the frequency spectrum contains the fault feature frequency, it shows that the faults exist in mechanical vibration signals. The analysis of the rolling bearing fault signals and the comparison with empirical model decomposition(EMD), it shows that the AMD method is effective and more rapid, accurate than EMD.

Key words: analytical mode decomposition(AMD), signal extraction, fault diagnosis, rotating machinery

摘要：

针对旋转机械故障诊断问题，提出了一种基于解析模态分解(AMD)的旋转机械故障诊断方法。只要知道信号的频率成分，AMD方法就可以将含不同频率成分的信号分解为单频率信号，尤其能够分解有紧密间隔频率成分的信号。对于可预知故障特征频率的旋转机械的故障诊断，可利用AMD方法提取机械振动信号中故障特征频率所在频段的信号，并求该段信号的频谱，若频谱中含有故障特征频率，则说明机械振动信号中存在该故障。通过对滚动轴承故障信号和转子不对中故障信号的分析以及和经验模态分解(EMD)方法的对比，证明了AMD方法的有效性，且AMD方法比EMD方法更快速、准确。

关键词: 解析模态分解, 信号提取, 故障诊断, 旋转机械

CLC Number:

Shi Peiming, Su Cuijiao, Zhao Na, Han Dongying. Fault Diagnosis Method for Rotating Machinery Based on AMD[J]. China Mechanical Engineering, 2016, 27(05): 674-679.

时培明, 苏翠娇, 赵娜, 韩东颖. 基于解析模态分解的机械故障诊断方法[J]. 中国机械工程, 2016, 27(05): 674-679.

References

[1]杨宇, 于德介, 程军圣. 基于经验模态分解的滚动轴承故障诊断方法[J]. 中国机械工程, 2004,15(10): 908-920.

Yang Yu, Yu Dejie, Cheng Yunsheng. Roller Bearing Fault Diagnosis Method Based on EMD[J]. China Mechanical Engineering, 2004, 15(10):908-920.

[2]王翔, 王仲生. 基于改进Hilbert-Huang变换的转子碰摩故障诊断[J]. 中国机械工程, 2010, 22(24): 2937-2940.

Wang Xiang, Wang Zhongsheng. Incipient Rub-impact Fault Diagnosis of Rotor System Using Improved HHT[J]. China Mechanical Engineering, 2011, 22(24):2937-2940.

[3]时培明, 李庚, 韩东颖. 基于改进EMD的旋转机械耦合故障诊断方法研究[J]. 中国机械工程, 2013, 24(17): 2367-2372.

Shi Peiming, Li Geng, Han Dongying. Study on Coupling Faults of Rotary Machinery Diagnosis Method based on Improved EMD[J]. China Mechanical Engineering, 2013, 24(17):2367-2372.

[4]Wu F, Qu L. An Improved Method for Restraining the End Effect in Empirical Mode Decomposition and Its Applications to the Fault Diagnosis of Large Rotating Machinery[J]. Journal of Sound and Vibration, 2008, 314(3): 586-602.

[5]Chen G, Wang Z. A Signal Decomposition Theorem with Hilbert Transform and Its Application to Narrowband Time Series with Closely Spaced Frequency Components[J]. Mechanical Systems and Signal Processing, 2012, 28: 258-279.

[6]李苗, 任伟新, 胡异丁, 等. 基于解析模态分解法的桥梁动态应变监测数据温度影响的分离[J]. 振动与冲击,2012, 31(21): 6-10.

Miao Li, Ren Weixin, Hu Yiding, et al. Separating Temperature Effect from Dynamic Strain Measurements of A Bridge Based on Analytical Mode Decomposition Method[J]. Journal of Vibration & Shock, 2012, 31(21): 6-10.

[7]康守强, 王玉静, 杨广学, 等. 基于经验模态分解和超球多类支持向量机的滚动轴承故障诊断方法[J]. 中国电机工程学报, 2011, 31(14): 96-102.

Kang Shouqiang, Wang Yujing, Yang Guangxue, et al. Rolling Bearing Fault Diagnosis Method Using Empirical Mode Decomposition and Hypersphere Multiclass Support Vector Machine[J]. Proceedings of the CSEE, 2011, 31(14):96-102.

[8]崔玲丽, 康晨晖, 胥永刚, 等. 滚动轴承早期冲击性故障特征提取的综合算法研究[J]. 仪器仪表学报, 2010, 31(11): 2422-2427.

Cui Lingli, Kang Chenhui, Xu Yonggang, et al. Integrated Algorithm Research on Early Impactive Fault Feature Extraction of Rolling Bearings[J]. Chinese Journal of Scientific Instrument, 2010, 31(11): 2422-2427.

[9]Feldman M. A Signal Decomposition or Lowpass Filtering with Hilbert Transform[J]. Mechanical Systems and Signal Processing, 2011, 25(8): 3205-3208.

[10]时培明, 李庚, 韩东颖, 等. 基于延拓和窗函数的HHT端点效应改进方法[J]. 计量学报, 2013, 34(5): 491-496.

Shi Peiming, Li Geng, Hai Dongying, et al. Extension and Window Function Based Improve Method for End Effects of Hilbert-Huang Transform[J]. Acta Metrologica Sinica, 2013, 34(5): 491-496.

[11]汤宝平, 董绍江, 马靖华. 基于独立分量分析的EMD模态混叠消除方法研究[J]. 仪器仪表学报, 2012, 33(7): 1477-1482.

Tang Baoping, Dong Shaojiang, Ma Jinghua. Study on the Method for Eliminating Mode Mixing of Empirical Mode Decomposition Based on Independent Component Analysis[J]. Chinese Journal of Scientific Instrument, 2012, 33(7): 1477-1482.

[1]	LUO Ji-Sai, XU De-Ge, SHI Mei-Li. Gearbox Fault Diagnosis with Rotating Speed Fluctuation Based on SVD and Chirplet Path Pursuit Algorithm [J]. J4, 201016, 21(16): 1947-1951.
[2]	ZHANG Xi-Ning, DIAO Meng, WEN An-Rui. A Multi-objective Optimization Method for Holo-balancing Weights of Rotor Set [J]. J4, 201016, 21(16): 1974-1977.
[3]	ZHENG Jinde, WANG Xinglong, PAN Haiyang, TONG Jinyu, LIU Qingyun. Rolling Bearing Fault Diagnosis Method Based on Adaptive Autogram [J]. China Mechanical Engineering, 2021, 32(07): 778-785，792.
[4]	CHEN Huanguo, LIU Peijun, YU Hang, XIAO Xue. Identification of Fault Degree of Oil Filter Blockage in Electro-hydraulic Actuator Based on an Improved PCA-SOM [J]. China Mechanical Engineering, 2021, 32(07): 799-805.
[5]	DING Jiaxin ;WANG Zhenya ;YAO Ligang ;CAI Yongwu. Rolling Bearing Fault Diagnosis Based on GCMWPE and Parameter Optimization SVM [J]. China Mechanical Engineering, 2021, 32(02): 147-155.
[6]	YANG Luhang1;LI Baoqing1;WANG Ping2,3;WANG Jian2,3;YANG Yu1. Fault Diagnosis Method of Rolling Bearings Based on Probability Output Flexible Convex Hull [J]. China Mechanical Engineering, 2021, 32(01): 40-46.
[7]	WAN Shuting;PENG Bo;WANG Xiaolong. Early Fault Diagnosis Method of Rolling Bearings Based on DTD Transform-SCS Method [J]. China Mechanical Engineering, 2020, 31(23): 2829-2836.
[8]	LI Feng1;CHENG Yangyang1,2;CHEN Yong1;TANG Baoping3. QAREDNN for State Trend Prediction of Rotating Machinery [J]. China Mechanical Engineering, 2020, 31(21): 2573-2582.
[9]	MENG Zong;LIU Zihan;LYU Meng. Fault Diagnosis for Rolling Bearings Based on Improved Singular Value Decomposition and Spectral Kurtosis [J]. China Mechanical Engineering, 2020, 31(20): 2420-2428.
[10]	WANG Zhenya;YAO Ligang. Rolling Bearing Fault Diagnosis Method Based on Generalized Refined Composite Multiscale Sample Entropy and Manifold Learning [J]. China Mechanical Engineering, 2020, 31(20): 2463-2471.
[11]	CAI Yanping;FAN Yu;CHEN Wan;ZHANG Jinming. Applications of Improved Time-frequency Analysis and Feature Fusion in Fault Diagnosis of IC Engines [J]. China Mechanical Engineering, 2020, 31(16): 1901-1911.
[12]	JIANG Shaofei;WU Tianji;PENG Xiang;LI Jiquan;LI Zhi;SUN Tao. Data Driven Fault Diagnosis Method Based on XGBoost Feature Extraction [J]. China Mechanical Engineering, 2020, 31(10): 1232-1239.
[13]	CHEN Xuefeng, GUO Yanjie, XU Caibin, SHANG Hongbing. Review of Fault Diagnosis and Health Monitoring for Wind Power Equipment [J]. China Mechanical Engineering, 2020, 31(02): 175-189.
[14]	LIU Yang;LIU Xiaobo;LIANG Shan. Aeroengine Rotor Fault Diagnosis Based on Fourier Decomposition Method [J]. China Mechanical Engineering, 2019, 30(18): 2156-2163.
[15]	LI Congzhi;ZHENG Jinde;PAN Haiyang;LIU Qingyun. Fault Diagnosis Method of Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Support Vector Machine [J]. China Mechanical Engineering, 2019, 30(14): 1713-1719,1726.