多源信息融合的离心式压缩机喘振诊断方法

doi:10.11949/0438-1157.20230454

摘要/Abstract

摘要：

离心式压缩机是炼油化工生产的关键动力设备，其一旦出现故障将可能造成重大生产事故，因此全生命周期的在线设备监测和故障诊断有助于化工生产的连续稳定运行。提出了一种基于流量、压力和振动等多源信息融合的离心式压缩机喘振诊断方法，首先利用经验小波变换将振动数据分解成指定数目的子频带信号并按照相关性排序进行信号重构，将重构的振动、流量、压力信号分别利用卷积神经网络进行预诊断，并将三种信号的归一化诊断结果采用加权的D-S证据理论进行最终诊断。通过离心式压缩机喘振模拟实验数据验证，利用提出的多源融合的故障诊断方法进行诊断，诊断精度可达97.25%，与使用单一传感器数据相比该方法显著提升了喘振故障诊断的容错能力，与其他多源融合方法相比诊断精度更高。

关键词: 离心式压缩机, 多源信息融合, 喘振诊断, 经验小波变换, 卷积神经网络, 加权D-S证据理论

Abstract:

The centrifugal compressor is the key power equipment for oil refining and chemical production. Once it breaks down, it may cause major plant's accidents. Therefore, online equipment monitoring and fault diagnosis throughout the life cycle is conducive to the continuous and stable operation of plant. This paper proposes a surge diagnosis method for centrifugal compressor based on multi-source data fusion such as flow, pressure and vibration. First, the vibration data is decomposed into a specified number of sub band signals by using empirical wavelet transform and the signal is reconstructed according to the correlation order. Convolutional neural network is used to pre-diagnose the reconstructed vibration signal, flow signal and pressure signal, and the final diagnosis is made using the weighted D-S evidence theory for the normalized diagnosis results of the three signals. Through the experiment of surge simulation test on centrifugal compressor, the diagnosis accuracy can reach 97.25% by using the multi-source fusion fault diagnosis method proposed in this paper. Compared with the use of single sensor data, this method significantly improves the fault tolerance ability of diagnosis, and it has higher diagnosis accuracy compared with other methods.

Key words: centrifugal compressor, muti-source data fusion, surge diagnosis, empirical wavelet transform, convolutional neural network, weighted D-S evidence theory

中图分类号:

U 226.8⁺1

徐野, 黄文君, 米俊芃, 申川川, 金建祥. 多源信息融合的离心式压缩机喘振诊断方法[J]. 化工学报, 2023, 74(7): 2979-2987.

Ye XU, Wenjun HUANG, Junpeng MI, Chuanchuan SHEN, Jianxiang JIN. Surge diagnosis method of centrifugal compressor based on multi-source data fusion[J]. CIESC Journal, 2023, 74(7): 2979-2987.

图/表 7

图1 离心式压缩机多源信息融合故障诊断方法

Fig.1 Fault diagnosis method of centrifugal compressor with multi-source data fusion

图2 单级离心压缩机实验台

Fig.2 Single stage centrifugal compressor test bench

图3 8000 r/min正常与喘振状态下离心式压缩机经验小波变换分解子频带与重构信号

Fig.3 Empirical wavelet transform decomposition sub band and reconstruction signal of centrifugal compressor under normal and surge conditions at 8000 r/min

表1 卷积神经网络模型参数

Table 1 Convolutional neural network model parameters

层号	层类型	参数说明	输入数据维度	输出数据维度
1	输入层	1000/5000	(1,1,1000) \ (1,1,5000)	(1,1,1000) \ (1,1,5000)
2	卷积层1	卷积核参数64×1，核数量32，步长为2	(1,1,1000) \ (1,1,5000)	(1,32,469) \ (1,32,2469)
3	池化层1	步长为2	(1,32,469) \ (1,32,2469)	(1,32,234) \ (1,32,1234)
4	卷积层2	卷积核参数3×32，核数量64，步长为2	(1,32,234) \ (1,32,1234)	(1,64,116) \ (1,32,616)
5	池化层2	步长为2	(1,64,116) \ (1,32,616)	(1,64,58) \ (1,64,308)
6	全连接层	—	(1,64,58) \ (1,64,308)	(1,2) \ (1,2)

表2 不同频带数下的多源信号诊断精度

Table 2 Diagnostic accuracy of multi-source signals under different frequency bands

数据来源	频带数2精度/%	频带数3精度/%	频带数4精度/%
振动	90.22±0.73	97.76±0.65	93.74±1.97
压力	95.48±2.87（原始数据）
流量	96.60±2.55（原始数据）
信号融合	95.81±2.18	97.25±1.56	95.11±2.13

图4 不同信号样本长度与子频带数下的多源融合诊断精度

Fig.4 Accuracy of multi-source data fusion diagnosis under different signal sample lengths and different numbers of sub bands

表3 不同方法下的诊断精度对比

Table 3 Comparison of diagnostic accuracy under different methods

方法类别	诊断精度/%
本文方法	98.05
未加入数据前处理模块的本文方法	94.87
经验模态分解下的本文方法	96.22
贝叶斯网络	92.76
D-S证据理论	90.28
多尺度置换熵	95.03

参考文献 5

5	Wang G B, He Z J, Chen X F, et al. Basic research on machinery fault diagnosis—what is the prescription[J]. Journal of Mechanical Engineering, 2013, 49(1): 63-72.
6	Rai A, Upadhyay S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International, 2016, 96: 289-306.
7	吕琛, 栾家辉, 王立梅, 等. 故障诊断与预测: 原理、技术及应用[M]. 北京: 北京航空航天大学出版社, 2012.
	Lv C, Luan J H, Wang L M, et al. Fault Diagnosis and Prediction: Principles, Techniques, and Applications [M]. Beijing: Beihang University Press, 2012.
8	马铭. 大型离心式压缩机组振动与控制研究[D]. 大庆: 东北石油大学, 2019.
	Ma M. Study on vibration and control of large centrifugal compressor unit[D].Daqing: Northeast Petroleum University, 2019.
9	杨晓俊, 朱兴龙. 基于局部平衡判别投影的往复压缩机故障诊断方法[J]. 机械设计与研究, 2022, 38(1): 215-218, 224.
	Yang X J, Zhu X L. Fault diagnosis method of reciprocating compressor based on locality balanced discriminant projection[J]. Machine Design & Research, 2022, 38(1): 215-218, 224.
10	潘云杰, 李颖, 王欣威, 等. 基于SPA和SQPE的往复压缩机滑动轴承故障特征提取方法[J]. 沈阳理工大学学报, 2022, 41(4): 20-25.
	Pan Y J, Li Y, Wang X W, et al. Fault feature extraction method of sliding bearing of reciprocating compressor based on SPA and SQPE[J]. Journal of Shenyang Ligong University, 2022, 41(4): 20-25.
11	李纯辉, 马永财, 徐国林, 等. 基于SVMFE的往复压缩机气阀故障诊断方法[J]. 噪声与振动控制, 2022, 42(5): 128-133.
	Li C H, Ma Y C, Xu G L, et al. Fault diagnosis method of reciprocating compressor valve based on SVMFE[J]. Noise and Vibration Control, 2022, 42(5): 128-133.
12	赵莹. 往复式压缩机的在线监测系统研究与设计[D]. 徐州: 中国矿业大学, 2020.
	Zhao Y. Research and design of on-line monitoring system for reciprocating compressor[D].Xuzhou: China University of Mining and Technology, 2020.
13	Li Y, Cheng G, Liu C. Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference[J]. Measurement, 2021, 169: 108509.
14	赵海洋, 韩辉, 王金东, 等. 基于小波包模糊熵的往复压缩机轴承故障特征提取方法研究[J]. 噪声与振动控制, 2018, 38(4): 18-22, 33.
	Zhao H Y, Han H, Wang J D, et al. A fault feature extraction method for reciprocating compressor bearings based on wavelet packet fuzzy entropy[J]. Noise and Vibration Control, 2018, 38(4): 18-22, 33.
15	Tang Y F, Wang T, Wang T, et al. Research on fault diagnosis of rolling bearing based on SEMSCNN and GRU model[J]. Journal of Physics: Conference Series, 2022, 2184(1): 012054.
16	Azamfar M, Singh J, Bravo-Imaz I, et al. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis[J]. Mechanical Systems and Signal Processing, 2020, 144: 106861.
17	王金东, 李颖, 赵海洋, 等. 基于VMD和MF-DFA的往复压缩机气阀故障特征提取方法[J]. 化工自动化及仪表, 2018, 45(6): 458-463.
	Wang J D, Li Y, Zhao H Y, et al. A feature extraction method for fault diagnosis of reciprocating compressor valve based on VMD and MF-DFA[J]. Control and Instruments in Chemical Industry, 2018, 45(6): 458-463.
18	鲁春燕, 李炜. 基于深度置信网络的炼化空压机故障诊断方法[J]. 化工学报, 2019, 70(2): 757-763.
	Lu C Y, Li W. Fault diagnosis method of petrochemical air compressor based on deep belief network[J]. CIESC Journal, 2019, 70(2): 757-763.
19	Liu C, Sun J Z, Wang F Y, et al. Bayesian network method for fault diagnosis of civil aircraft environment control system[J]. Systems and Control Engineering, 2019, 234(5): 1-13.
20	Askarian M, Zarghami R, Jalali-Farahani F, et al. Fusion of micro-macro data for fault diagnosis of a sweetening unit using Bayesian network[J]. Chemical Engineering Research and Design, 2016, 115: 325-334.
21	Liu Y W, Cheng Y Q, Zhang Z Z, et al. Multi-information fusion fault diagnosis based on KNN and improved evidence theory[J]. Journal of Vibration Engineering & Technologies, 2022, 10(3): 841-852.
22	Jiang W, Xie C H, Zhuang M Y, et al. Sensor data fusion with Z-numbers and its application in fault diagnosis[J]. Sensors, 2016, 16(9): 1509.
23	Ma S L, Jia B W, Wu J W, et al. Multi-vibration information fusion for detection of HVCB faults using CART and D-S evidence theory[J]. ISA Transactions, 2021, 113: 210-221.
24	Zhou D J, Wei T T, Zhang H S, et al. An information fusion model based on Dempster-Shafer evidence theory for equipment diagnosis[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2018, 4(2): 021005.
25	吴耀春, 赵荣珍, 靳伍银. EWT与加权多邻域粗糙集结合的旋转机械故障特征提取方法[J]. 振动与冲击, 2019, 38(24): 235-242.
	Wu Y C, Zhao R Z, Jin W Y. Fault feature extraction of rotating machinery based on EWT and a weighted multi neighborhood rough set[J]. Journal of Vibration and Shock, 2019, 38(24): 235-242.
26	蔡艳平, 李艾华, 王涛, 等. 基于EMD-Wigner-Ville的内燃机振动时频分析[J]. 振动工程学报, 2010, 23(4): 430-437.
	Cai Y P, Li A H, Wang T, et al. I.C.engine vibration time-frequency analysis based on EMD-Wigner-Ville[J]. Journal of Vibration Engineering, 2010, 23(4): 430-437.
27	王江萍, 王潇, 鲍泽富. 基于信息融合理论的柴油机故障诊断技术[J]. 石油机械, 2010, 38(6): 49-52, 72, 104.
	Wang J P, Wang X, Bao Z F. The information fusion theory-based failure diagnosis technology of diesel engine[J]. China Petroleum Machinery, 2010, 38(6): 49-52, 72, 104.
28	Yager R R. On the Dempster-Shafer framework and new combination rules[J]. Information Sciences, 1987, 41(2): 93-137.
29	Zhang Y Y, Shi J, Wang S P, et al. A multi-source information fusion fault diagnosis method for vectoring nozzle control system based on Bayesian network[C]//2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM). Vancouver, BC, Canada: IEEE, 2020: 1-6.
30	Huo Z Q. Bearing fault diagnosis using multi-sensor fusion based on weighted DS evidence theory[C]//18th International Conference on Mechatronics - Mechatronika (ME). 2018.
1	雷亚国, 贾峰, 周昕, 等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21): 49-56.
	Lei Y G, Jia F, Zhou X, et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56.
2	张婷婷, 贾铭钰. 机械设备故障诊断技术的常用方法及新技术的应用研究[J]. 自动化与仪器仪表, 2017(10): 38-39, 42.
	Zhang T T, Jia M Y. Research on common methods and application of new technology in fault diagnosis of mechanical equipment[J]. Automation & Instrumentation, 2017(10): 38-39, 42.
3	辛斌. 往复压缩机故障诊断技术研究[J]. 当代化工研究, 2018(6): 70-71.
	Xin B. Research on fault diagnosis method of reciprocating compressors[J]. Modern Chemical Research, 2018(6): 70-71.
4	张弘. 离心压缩机研究现状与展望[J]. 化工管理, 2016(32): 267, 125.
	Zhang H. Research status and prospect of centrifugal compressor[J]. Chemical Engineering Management, 2016(32): 267, 125.
5	王国彪, 何正嘉, 陈雪峰, 等. 机械故障诊断基础研究“何去何从”[J]. 机械工程学报, 2013, 49(1): 63-72.

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