赵海峰,张家骏,吕建卓
ZHAO Haifeng,ZHANG Jiajun,LYU Jianzhuo

• 1:东北石油大学机械科学与工程学院

摘要(Abstract)：

针对传统信号处理方法难以准确提取往复压缩机气阀振动信号故障特征的问题，引入一种新的非线性分析方法——散布熵，以实测往复压缩机气阀振动信号为研究对象，从鲁棒性与稳定性两方面进行了适用性分析研究。首先，通过对工频干扰消除前后的阀片断裂信号，以及添加不同信噪比高斯白噪声的正常气阀信号进行鲁棒性分析；然后引入变异系数对气阀正常、阀片断裂及弹簧失效三种状态的振动信号进行稳定性分析；最后在该基础上进行特征提取研究。结果表明：散布熵不仅对工频干扰不敏感，而且在信噪比0 dB时的熵值变化率不超过5%，气阀3种状态的散布熵变异系数均低于1%，从而为往复压缩机气阀状态监测与故障诊断提供了一个鲁棒性强、稳定性好的有效特征。
For the problem of the difficulty of the traditional signal processing method in accurately extracting the fault features of the vibration signal of the reciprocating compressor valve，a new nonlinear analysis method-dispersion entropy was introduced.By taking the measured vibration signal of the reciprocating compressor valve as the research object，the applicability analysis was carried out from robustness and stability.Firstly，the robustness analysis was carried out on the valve plate fracture signal before and after the elimination of power frequency interference，and the normal valve signal added with different SNR Gaussian white noise.Then，the coefficient of variation was introduced to analyze the stability of the vibration signals of the normal valve，the valve plate fracture and the spring failure.Finally，feature extraction was studied based on this.The results show that the dispersion entropy is not sensitive to the power frequency interference，and the change rate of the entropy value is less than 5% when the SNR is 0 dB，and the coefficients of variation of the dispersion entropy in the three states of the valves are all less than 1%，which provides an effective feature of good robustness and stability for the state monitoring and fault diagnosis of the reciprocating compressor valve.

关键词(KeyWords)： 往复压缩机;气阀;散布熵;样本熵;特征提取
reciprocating compressor;gas valve;dispersion entropy;sample entropy;feature extraction

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(11472076)

作者(Author): 赵海峰,张家骏,吕建卓
ZHAO Haifeng,ZHANG Jiajun,LYU Jianzhuo

参考文献(References)：

• [1]余良俭.往复压缩机故障诊断技术现状与发展趋势[J].流体机械，2014，42(1)：36-39.YU L J.Status and development trend of reciprocating compressor fault diagnosis technology[J].Fluid Machinery，2014，42(1)：36-39.
• [2]CUI H，ZHANG L，KANG R，et al.Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method[J].Journal of Loss Prevention in the Process Industries，2009，22(6)：864-867.
• [3]孙旭，刘晓明，李磊，等.往复压缩机流量调控工况下排气阀泄漏仿真研究[J].流体机械，2021，49(8)：100-104.SUN X，LIU X M，LI L，et al.Simulation study on exhaust valve leakage under capacity control of reciprocating compressor[J].Fluid Machinery，2021，49(8)：100-104.
• [4]韩宝坤，张冬鸣，张国伟，等.基于流固耦合法的往复压缩机吸气阀流场特性研究[J].流体机械，2021，49(5)：47-53.HAN B K，ZHANG D M，ZHANG G W，et al.Research on flow field characteristics of suction valve of reciprocating compressor based on fluid-structure coupling method[J].Fluid Machinery，2021，49(5)：47-53.
• [5]肖军，舒悦，谢传东，等.基于二叉树支持向量机的往复压缩机气阀故障诊断方法[J].流体机械，2020，48(10)：48-53.XIAO J，SHU Y，XIE C D，et al.Fault diagnosis of reciprocating compressor gas valve based on binary tree support vector machine[J].Fluid Machinery，2020，48(10)：48-53.
• [6]史勇，郭俊先，李雪莲，等.哈密瓜无损检测技术及分级装备的研究现状和发展趋势[J].包装与食品机械，2021，39(6)：75-82.SHI Y，GUO J X，LI X L，et al.Research status and development trend of non-destructive testing technology and grading equipment for Hami melon[J].Packaging and Food Machinery，2021，39(6)：75-82.
• [7]刘树林，杨丽晶，马锐，等.近似熵在往复压缩机气阀故障诊断中的应用[J].压缩机技术，2005(6)：20-22.LIU S L，YANG L J，MA R，et al.Application of approximate entropy in reciprocating compressor valve fault diagnosis[J].Compressor Technology，2005(6)：20-22.
• [8]张思阳，徐敏强，王日新，等.EMD与样本熵在往复压缩机气阀故障诊断中的应用[J].哈尔滨工程大学学报，2014(6)：696-700.ZHANG S Y，XU M Q，WANG R X，et al.Application of EMD and sample entropy in reciprocating compressor valve fault diagnosis[J].Journal of Harbin Engineering University，2014(6)：696-700.
• [9]MUA X D，WUB L，ZHAO H.A Reciprocating compressor fault feature extraction method based on LMD and MPE[M]//Material Engineering and Mechanical Engineering：Proceedings of Material Engineering and Mechanical Engineering (MEES2015)，2016：705-713.
• [10]陈强强，戴邵武，戴洪德，等.滚动轴承故障诊断方法综述[J].仪表技术，2019(9)：43-46.CHEN Q Q，DAI S W，DAI H D，et al.Summary of rolling bearing fault diagnosis methods[J].Instrument Technology，2019(9)：43-46.
• [11]LI Y，WANG X，LIU Z，et al.The entropy algorithm and its variants in the fault diagnosis of rotating machinery：a review[J].IEEE Access，2018，6：66723-66741.
• [12]ROSTAGHI M，ASHORY M R，AZAMI H.Application of dispersion entropy to status characterization of rotary machines[J].Journal of Sound and Vibration，2019，438：291-308.
• [13]AZAMI H，ESCUDERO J.Amplitude-and fluctuation-based dispersion entropy[J].Entropy，2018，20(3)：210.
• [14]ROSTAGHI M，AZAMI H.Dispersion entropy：a measure for time-series analysis[J].IEEE Signal Processing Letters，2016，23(5)：610-614.
• [15]付文龙，谭佳文，王凯.基于VMD散布熵与改进灰狼优化SVDD的轴承半监督故障诊断研究[J].振动与冲击，2019，38(22)：190-197.FU W L，TAN J W，WANG KAI.Research on semi-supervised fault diagnosis of bearings based on VMD dispersion entropy and improved grey wolf optimized SVDD[J].Journal of Vibration and Shock，2019，38(22)：190-197.
• [16]吴守军，冯辅周，吴春志，等.基于VMD-DE的坦克行星变速箱故障诊断方法研究[J].振动与冲击，2020，39(10)：170-179.WU S J，FENG F Z，WU C Z，et al.Research on fault diagnosis method of tank planetary gearbox based on VMD-DE[J].Journal of Vibration and Shock，2020，39(10)：170-179.
• [17]曹颜玉，龙瑶妹，赵刚，等.不同调控方式下宽工况压缩机适应性分析[J].化工设备与管道，2021，58(6)：39-45.CAO Y Y，LONG Y M，ZHAO G，et al.Adaptability analysis for compressor working under wide operating condition with different regulation methods[J].Process Equipment & Piping，2021，58(6)：39-45.
• [18]陈宇晨，何毅斌，戴乔森，等.基于EMDD信息量和KNP-SVDD的滚动轴承故障诊断研究[J].机电工程，2021，38(1)：55-61.CHEN Y C，HE Y B，DAI Q S，et al.Fault diagnosis of rolling bearing based on EMDD information and KNP-SVDD[J].Journal of Mechanical & Electrical Engineering，2021，38(1)：55-61.
• [19]郑一珍，牛蔺楷，熊晓燕，等.基于EMD-SDP特征融合的CNN轴承保持架故障诊断研究[J].机电工程，2021，38(1)：81-87.ZHENG Y Z，NIU L K，XIONG X Y，et al.Fault diagnosis of CNN bearing cage based on EMD-SDP feature fusion[J].Journal of Mechanical & Electrical Engineering，2021，38(1)：81-87.
• [20]任学平，霍灿鹏.基于EMD-AR谱和GA-BP的滚动轴承故障诊断研究[J].机电工程，2021，38(7)：892-896.REN X P，HUO C P.Fault diagnosis of rolling bearing based on EMD-AR spectrum and GA-BP[J].Journal of Mechanical & Electrical Engineering，2021，38(7)：892-896.
• [21]高晶波，徐敏强，张嘉钟.2D12型往复式压缩机气阀故障诊断的时频分析方法[J].化工机械，2003(3)：200-202.GAO J B，XU M Q，ZHANG J Z.Time-frequency analysis method for valve fault diagnosis of 2D12 reciproating compressor[J].Chemical Machinery，2003(3)：200-202.
• [22]唐友福，刘树林，刘颖慧，等.基于非线性复杂测度的往复压缩机故障诊断[J].机械工程学报，2012，48(3)：102-107.TANG Y F，LIU S L，LIU Y H，et al.Fault diagnosis of reciprocating compressor based on nonlinear complex measure[J].Journal of Mechanical Engineering，2012，48(3)：102-107.
• [23]李伟光，张晓涛.基于奇异向量频谱的工频干扰消除方法[J].华南理工大学学报(自然科学版)，2017，45(5)：16-23.LI W G，ZHANG X T.Power frequency interference elimination method based on singular vector spectrum[J].Journal of South China University of Technology (Natural Science Edition)，2017，45(5)：16-23.
• [24]RICHMAN J S，MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology Heart and Circulatory Physiology，2000，278(6)：2039-2049.
• [25]王华中，魏晖，高志刚.基于盒维数序列变异系数的轴承外圈失效特征[J].机械设计与制造，2018(10)：149-152.WANG H Z，WEI H，GAO Z G.Failure characteristics of bearing outer ring based on variation coefficients of box dimension sequence[J].Machinery Design & Manufacture，2018(10)：149-152.