Application of Local Mean Decomposition and 1.5 Dimension Spectrum in Machinery Fault Diagnosis

China Mechanical Engineering ›› 2013, Vol. 24 ›› Issue (4): 452-457.

Previous Articles Next Articles

Application of Local Mean Decomposition and 1.5 Dimension Spectrum in Machinery Fault Diagnosis

Zhong Xianyou1,2;Zeng Liangcai1;Zhao Chunhua2
1.Education Ministry Key Laboratory of Metallurgical Equipment and Control,Wuhan University of Science and Technology,Wuhan,430081
2.Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance,China Three Gorges University,Yichang,Hubei,443002

Online:2013-02-25 Published:2013-02-28
Supported by:
National Natural Science Foundation of China（No. 51075234）

局域均值分解和1.5维谱在机械故障诊断中的应用

钟先友1,2;曾良才1;赵春华2

1.武汉科技大学冶金装备及其控制教育部重点实验室,武汉,430081
2.三峡大学水电机械设备设计与维护湖北省重点实验室,宜昌,443002

基金资助:
国家自然科学基金资助项目(51075234)
National Natural Science Foundation of China（No. 51075234）

Abstract

Abstract:

Aiming at nonlinear,non-stationary characteristics of a mechanical failure vibration signal,a mechanical fault diagnosis method combining LMD and 1.5 dimensional spectrum was proposed.In this approach,LMD method was applied to decompose the original signals into a finite number of product functions(PFs),then the PFs containing fault information were analyzed with 1.5 dimension spectrum to extract the characteristics.This method was endowed with characteristics of suppressing Gaussian white noise,detecting the nonlinear coupling feature.The method was verified by simulation signals and engineering examples of mechanical fault diagnosis effectively.

Key words: local mean decomposition(LMD), 1.5 dimension spectrum, characteristic frequency, fault diagnosis

摘要：

针对机械故障振动信号的非线性、非平稳特征,提出了局域均值分解和1.5维谱相结合的机械故障诊断方法。该方法首先对信号进行局域均值分解，将其分解为若干个PF分量之和,然后运用1.5维谱方法对含有故障特征信息的PF分量进行特征提取。该方法具有抑制高斯白噪声、检测非线性耦合特征等特性。仿真信号与机械故障诊断工程实例的分析验证了该方法的有效性。


关键词: 局域均值分解, 1.5维谱, 特征频率, 故障诊断

CLC Number:

ZHONG Xian-You-1, 2, CENG Liang-Cai-1, DIAO Chun-Hua-2. Application of Local Mean Decomposition and 1.5 Dimension Spectrum in Machinery Fault Diagnosis[J]. China Mechanical Engineering, 2013, 24(4): 452-457.

钟先友1, 2, 曾良才1, 赵春华2. 局域均值分解和1.5维谱在机械故障诊断中的应用[J]. 中国机械工程, 2013, 24(4): 452-457.

[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]	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.
[3]	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.
[4]	WANG Zhiqiang, , LEI Zhenyu, . Study on Influence Parameters of Rail Corrugation on Tangential Tracks of Metro [J]. China Mechanical Engineering, 2021, 32(04): 420-430.
[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]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	LIU Yang;LIU Xiaobo;LIANG Shan. Aeroengine Rotor Fault Diagnosis Based on Fourier Decomposition Method [J]. China Mechanical Engineering, 2019, 30(18): 2156-2163.
[14]	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.
[15]	LIU Zhaolun1,2;ZHANG Chunlan2;WU You2;WANG Haiyu2;LIU Bin1,2. An Incremental Bayesian Algorithm on Fault Diagnosis of Grate Coolers [J]. China Mechanical Engineering, 2019, 30(10): 1163-1171.

Application of Local Mean Decomposition and 1.5 Dimension Spectrum in Machinery Fault Diagnosis

局域均值分解和1.5维谱在机械故障诊断中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics