基于变分循环冲击模态分解的氢气压缩机增压缸同型多源混叠信号分析方法

doi:10.3901/JME.260695

摘要/Abstract

摘要： 针对液驱往复式氢气压缩机增压缸振动信号存在同型多源混叠的问题，提出了一种新型自适应分解方法——变分循环冲击模态分解。该方法结合了变分模型和循环冲击模态的特点，通过优化迭代过程，有效地分解了同型多源混叠振动信号，并提取与故障相关的有效信息。变分循环冲击模态分解假设信号中的循环冲击模态及干扰成分的能量集中在有限带宽范围内，并以固定的周期重复出现峰值。通过最小化每个模态的频率和时间中心偏移的L2范数之和，并以每个循环冲击模态的和与原始信号之间的误差为约束条件，实现了同型信号的分解。与传统方法相比，变分循环冲击模态分解能够有效处理同型多源混叠信号，分解出单缸活塞的冲击信号。针对液驱往复式氢气压缩机增压缸诊断指标中故障机理缺失的问题，提出了活塞振动谐波幅值累加和指标，通过提取活塞往复频率及其谐波的幅值，实现了对活塞故障的检测。最后，在液驱往复式氢气压缩机试验平台的测试与分析结果表明，结合活塞振动谐波幅值累加和指标的变分循环冲击模态分解诊断方法具备较强的可行性，并且在准确率和精确度方面优于现有方法，验证了其有效性和先进性。

关键词: 同型多源混叠, 变分循环冲击模态分解, 振动分析, 增压缸故障诊断, 氢气压缩机

Abstract: The issue of multi-source mixing in the vibration signals of the booster cylinder of hydraulic-driven reciprocating hydrogen compressors is addressed. A novel adaptive decomposition method, called variational cyclic impact mode decomposition (VCIMD), is proposed. This method combines the characteristics of variational models and cyclic impact modes, and through an optimized iterative process, it effectively decomposes multi-source mixed vibration signals, extracting relevant fault-related information. VCIMD assumes that the energy of cyclic impact modes and interference components in the signal is concentrated within a limited bandwidth and exhibits peak values that repeat at fixed periods. By minimizing the sum of the L2 norms of frequency and time center shifts for each mode, and constraining the error between the sum of cyclic impact modes and the original signal, the decomposition of the mixed signals is achieved. Compared to traditional methods, VCIMD can effectively handle multi-source mixed signals and decompose the impact signals from the single-cylinder piston. To address the lack of fault mechanism indicators in the diagnostic process for the booster cylinder of hydraulic-driven reciprocating hydrogen compressors, a Piston Vibration Harmonic Amplitude Accumulation Index (PVHAAI) is proposed. By extracting the amplitude of the piston’s reciprocating frequency and its harmonics, this index enables the detection of piston faults. Finally, the experimental results from a real hydraulic-driven reciprocating hydrogen compressor test platform demonstrate that the VCIMD method, combined with the PVHAAI, is highly feasible. It outperforms existing methods in terms of accuracy and precision, validating its effectiveness and advancement.

Key words: homogeneous multi-source mixing, variational cyclical impulse mode decomposition, vibration analysis, booster cylinder fault diagnosis, hydrogen compressor

中图分类号:

TG156

刘志亮, 张潇楠, 陈志鹏, 唐瑜君, 梅松政, 钟春波, 顾小明, 左明健. 基于变分循环冲击模态分解的氢气压缩机增压缸同型多源混叠信号分析方法[J]. 机械工程学报, 2026, 62(13): 193-206.

LIU Zhiliang, ZHANG Xiaonan, CHEN Zhipeng, TANG Yujun, MEI Songzheng, ZHONG Chunbo, GU Xiaoming, ZUO Mingjian. Analysis of Homotypic Multi-source Mixed Signal for the Booster Cylinder of Hydrogen Compressors Based on Variational Cyclic Impulse Mode Decomposition[J]. Journal of Mechanical Engineering, 2026, 62(13): 193-206.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I13/193

参考文献

[1] 邵志刚，衣宝廉. 氢能与燃料电池发展现状及展望[J]. 中国科学院院刊，2019，34(04)：469-477. SHAO Zhigang，YI Baolian. Developing trend and present status of hydrogen energy and fuel cell development[J]. Bulletin of the Chinese Academy of Sciences，2019，34(04)：469-477.
[2] 王意东，许苏予，何太碧，等. 中国氢能及燃料电池产业政策研究及启示[J]. 天然气工业，2024，44(5)：136-145. WANG Yidong，XU Suyu，HE Taibi，et al. Research and implication of China's hydrogen energy and fuel cell industrial policies[J]. Natural Gas Industry，2024，44(5)：136-145.
[3] 陈晔，刘晅亚，纪超. 加氢站事故风险与消防安全技术研究[C]//2019中国消防协会科学技术年会论文集. 北京：中国消防协会，2019:3. CHEN Ye，LIU Xuanya，JI Chao. Research on accident risk and fire safety technology of hydrogen refueling stations[C]//2019 China Fire Protection Association Science and Technology Annual Conference Proceedings. Beijing：China Fire Protection Association，2019:3.
[4] 肖强，张乐，张建军，等. 大规模地下储氢用压缩机现状与发展趋势[J]. 流体机械，2024，52(2)：75-82. XIAO Qiang，ZHANG Le，ZHANG Jianjun，et al. Current situation and development trends in compressors for use in large-scale underground hydrogen storage[J]. Fluid Machinery，2024，52(2)：75-82.
[5] 李雪莹，任鹏，彭学院，等. 加氢站压缩机无损监测方法与故障诊断系统[J]. 机械工程学报，2023，59(22)：186-195. LI Xueying，REN Peng，PENG Xueyuan，et al. Non-invasive condition monitoring method and fault diagnosis system for compressor in hydrogen refueling station[J]. Journal of Mechanical Engineering，2023，59(22)：186-195.
[6] 刘华敏，吕倩，余小玲，等. 基于人工智能的往复式压缩机故障诊断研究综述[J]. 流体机械，2020，48(09)：65-70+82. LIU Huamin，LÜ Qian，YU Xiaoling，et al. Research review on fault diagnosis of reciprocating compressor based on artificial intelligence[J]. Fluid Machinery，2020，48(09)：65-70+82.
[7] LI Zheren，LIU Zhiliang，ZUO Mingjian. Homotypic multi-source mixed signal decomposition based on maximum time-shift kurtosis for drilling pump fault diagnosis[J]. Mechanical Systems and Signal Processing，2024，221：111724.
[8] HUANG N E，SHEN Z，LONG S R，et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:mathematical，Physical and Engineering Sciences，1998，454(1971)：903-995.
[9] DRAGOMIRETSKIY K，ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing，2013，62(3)：531-544.
[10] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing，2013，61(16)：3999-4010.
[11] MIAO Y，ZHANG B，LI C，et al. Feature mode decomposition: new decomposition theory for rotating machinery fault diagnosis[J]. IEEE Transactions on Industrial Electronics，2022，70(2)：1949-1960.
[12] HOU B，XIE M，YAN H，et al. Impulsive mode decomposition[J]. Mechanical Systems and Signal Processing，2024，211：111227.
[13] YU G. A concentrated time–frequency analysis tool for bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement，2019，69(2)：371-381.
[14] LIU Z，PENG D，ZUO M J，et al. Improved Hilbert–Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings[J]. ISA Transactions，2022，125：426-444.
[15] PANG B，NAZARI M，TANG G. Recursive variational mode extraction and its application in rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing，2022，165：108321.
[16] CHEGINI S N，BAGHERI A，NAJAFI F. Application of a new EWT-based denoising technique in bearing fault diagnosis[J]. Measurement，2019，144：275-297.
[17] ZHAO N，ZHANG J，MA W，et al. Variational time-domain decomposition of reciprocating machine multi-impact vibration signals[J]. Mechanical Systems and Signal Processing，2022，172：108977.
[18] ZHAO N，ZHANG J，MAO Z，et al. Variational time–frequency adaptive decomposition of machine multi-impact vibration signals[J]. Mechanical Systems and Signal Processing，2023，189：110084.
[19] CHEN Y，ZHANG J，ZHAO N，et al. Impact time domain decomposition:An adaptive decomposition method for multi-source impact signals based on envelope energy gradient characteristics[J]. Mechanical Systems and Signal Processing，2024，219：111637.
[20] DENG F，QIANG Y，YANG S，et al. Sparse representation of parametric dictionary based on fault impact matching for wheelset bearing fault diagnosis[J]. ISA Transactions，2021，110：368-378.
[21] ZHANG X，MIAO Q，ZHANG H，et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing，2018，108：58-72.
[22] DENG B，YU G，LIN T，et al. Fast cmspogram: an effective new tool for periodic pulse detection[J]. Mechanical Systems and Signal Processing，2024，209：111094.
[23] YU Y，ZHAO X. Separation of fault characteristic impulses of flexible thin-wall bearing based on wavelet transform and correlated Gini index[J]. Mechanical Systems and Signal Processing，2024，209：111118.
[24] ANTONI J. Cyclic spectral analysis in practice[J]. Mechanical Systems and Signal Processing，2007，21(2)：597-630.
[25] 李喆仁，刘志亮，廖飞龙，等. 基于泵送频率谐波幅值累加和的钻井泵液力端凡尔体故障诊断方法[J]. 失效分析与预防，2024，19(4)：225-232. LI Zheren，LIU Zhiliang，LIAO Feilong，et al. Fault diagnosis method for hydraulic end of drilling pump valve based on harmonic amplitude summation of pump stroke frequency[J]. Failure Analysis and Prevention，2024，19(4)：225-232.
[26] 李喆仁，刘志亮，莫巍，等. 基于泵送频率的往复泵活塞故障诊断方法[J]. 流体机械，2024，52(04)：95-104. LI Zheren，LIU Zhiliang，MO Wei，et al. Fault diagnosis method based on pump stroke frequency for reciprocating pump piston[J]. Fluid Machinery，2024，52(04)：95-104.
[27] 李喆仁，刘志亮，廖飞龙，等. 往复式钻井泵的阀门故障诊断方法[J]. 失效分析与预防，2024，19(01)：13-19+67. LI Zheren，LIU Zhiliang，LIAO Feilong，et al. Method for diagnosing valve failures in reciprocating drilling pumps[J]. Failure Analysis and Prevention，2024，19(01)：13-19+67.
[28] 邓尧曦，王浩宇，吕咏学. 压缩机活塞密封结构与性能分析[J]. 压缩机技术，2018(2)：17-21. DENG Yaoxi，WANG Haoyu，LV Yongxue. Piston seal structure and performance analysis of compressor[J]. Compressor Technology，2018(2)：17-21.
[29] 陈志鹏，刘志亮，张潇楠，等. 基于振动分析的液驱往复式氢气压缩机压缩周期参数非直接测量方法[J]. 工程科学与技术，2024，56(06)：82-92. CHEN Zhipeng，LIU Zhiliang，ZHANG Xiaonan，et al. Indirect measurement method of compression cycle parameters of liquid-driven reciprocating hydrogen compressor based on vibration analysis[J]. Advanced Engineering Sciences，2024，56(06)：82-92.