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.
|