Local Mean Decomposition Based on Rational Hermite Interpolation and Its Application for Fault Diagnosis of Reciprocating Compressor

doi:10.3901/JME.2015.01.083

Abstract

Abstract: According to the strongly non-stationary characteristic of reciprocating compressor vibration signal, a local mean decomposition (LMD) method based on rational Hermite interpolation is proposed. Due to the excellent characteristic of shape preservation and characteristic of adjustable curve shape with parameter, Hermite rational interpolation method is used to construct envelope curves between extreme points. In order to improve the envelope approximation accuracy of local mean and envelope estimation, the local envelope curves optimization method which taken extreme value symmetry points as criterion is proposed. Algorithms and processes of rational Hermite interpolation LMD method are given. Compared with results of different interpolation methods for simulation data, superiority of this method is proved. Taken vibration signal of reciprocating compressor bearing fault as research objects, oversized bearing clearance fault is diagnosed accurately by this method, and the applicability of this method for strongly non-stationary signal is verified.

Key words: fault diagnosis, interpolation envelope, local mean decomposition, rational Hermite, reciprocating compressor

CLC Number:

TH165

ZHAO Haiyang,XU Minqiang,WANG Jindong. Local Mean Decomposition Based on Rational Hermite Interpolation and Its Application for Fault Diagnosis of Reciprocating Compressor[J]. Journal of Mechanical Engineering, 2015, 51(1): 83-89.

[1]	DI Zijun, YUAN Dongfeng, LI Dongyang, LIANG Daojun, ZHOU Xiaotian, XIN Miaomiao, CAO Feng, LEI Tengfei. Tool Fault Diagnosis Method Based on Multiscale-efficient Channel Attention Network [J]. Journal of Mechanical Engineering, 2024, 60(6): 82-90.
[2]	YAN Ruqiang, XU Wengang, WANG Zhiying, ZHU Qixiang, ZHOU Zheng, ZHAO Zhibin, SUN Chuang, WANG Shibin, CHEN Xuefeng, ZHANG Junhui, XU Bing. Research Status and Challenges on Fault Diagnosis Methodology for Fuel Control System of Aero-engine [J]. Journal of Mechanical Engineering, 2024, 60(4): 3-31.
[3]	ZHANG Junhui, LIU Shihao, XU Bing, HUANG Weidi, LÜ Fei, HUANG Xiaochen. Research Status and Development Trends on Intelligent Key Technology of the Axial Piston Pump [J]. Journal of Mechanical Engineering, 2024, 60(4): 32-49.
[4]	DING Ruqi, XIONG Wenjie, CHENG Min, XU Bing. Safety Performance Evaluation of the Intelligent Independent-metering Electro-hydraulic Control System [J]. Journal of Mechanical Engineering, 2024, 60(4): 101-112.
[5]	WANG Zhiying, LI Tianfu, XU Wengang, SUN Chuang, ZHANG Junhui, XU Bing, YAN Ruqiang. Denoising Mixed Attention Variational Auto-encoder for Axial Piston Pump Fault Diagnosis [J]. Journal of Mechanical Engineering, 2024, 60(4): 167-177.
[6]	SHAO Haidong, LIN Jian, MIN Zhishan, MING Yuhang. Improved Semi-supervised Prototype Network for Cross-domain Fault Diagnosis of Gearbox under Out-of-distribution Interference Samples [J]. Journal of Mechanical Engineering, 2024, 60(4): 212-221.
[7]	PAN Haiyang, XU Haifeng, ZHENG Jinde, TONG Jinyu, ZHANG Feibin. Mechanical Fault Diagnosis Method Based on Twin Weighted Imbalanced Matrix Classifier [J]. Journal of Mechanical Engineering, 2024, 60(3): 170-180.
[8]	ZHAO Ke, YE Min, WANG Ruixin, LU Hai, LIU Mengmeng, SHAO Haidong. Fuzzy Domain Adaptation Approach for Source-free Domain Rotary Machinery Fault Diagnosis [J]. Journal of Mechanical Engineering, 2024, 60(18): 43-52.
[9]	LI Zhipeng, MA Tianyu, LIU Jinping, XIANG Qingsong, TANG Junjie. Multi-source Domain Adaptation Intelligent Fault Diagnosis Method Based on Asymmetric Adversarial Training [J]. Journal of Mechanical Engineering, 2024, 60(18): 76-88.
[10]	WANG Yukun, YI Cai, WANG Hao, ZHOU Qiuyang, RAN Le, WANG Jingyuan. Adaptive Frequency Band Division Method Guided by PSD and Its Application in Bearing Fault Diagnosis [J]. Journal of Mechanical Engineering, 2024, 60(17): 179-193.
[11]	LIU Yilong, LI Xinyuan, CHEN Yinping, CHENG Wei, CHEN Xuefeng. A Motor Bearing Cage Fault Diagnosis Method Based on Local Maximum of Kurtosis Surface [J]. Journal of Mechanical Engineering, 2024, 60(15): 89-99.
[12]	YU Xiaoluo, YANG Yang, DU Minggang, HE Qingbo, PENG Zhike. Cooperative Detection and Decomposition Approach of Deviation-included Frequency Components for Mechanical Transmission Systems [J]. Journal of Mechanical Engineering, 2024, 60(15): 100-112.
[13]	HUANG Baoyu, ZHANG Yongxiang. Rolling Element Bearing Fault Diagnosis Using a Three-step Scheme [J]. Journal of Mechanical Engineering, 2024, 60(14): 51-68.
[14]	CHEN Qian, CHEN Kangkang, DONG Xingjian, HUANGFU Yifan, PENG Zhike, MENG Guang. Interpretable Convolutional Neural Network for Mechanical Equipment Fault Diagnosis [J]. Journal of Mechanical Engineering, 2024, 60(12): 65-76.
[15]	WANG Shibin, WANG Shiao, CHEN Xuefeng, HUANG Hai, AN Botao, ZHAO Zhibin, LIU Yongquan, LI Yinghong. Interpretable Network Construction for Intelligent Monitoring and Diagnosis,and Application in Inter-shaft Bearing Diagnosis While Aero-engine Test [J]. Journal of Mechanical Engineering, 2024, 60(12): 90-106.

Local Mean Decomposition Based on Rational Hermite Interpolation and Its Application for Fault Diagnosis of Reciprocating Compressor

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments