Research on Incipient Warning and Diagnosis Method of Sudden Unbalance Fault for Rotating Machinery

doi:10.3901/JME.2023.11.308

Abstract

Abstract: Incipient warning of sudden unbalance faults for rotating machinery is a difficult problem in engineering practice in the field of current condition monitoring, and it is of great significance to avoid disaster accidents caused by unplanned downtime of equipment. Aiming at the situation of abundant normal data and lack of fault data in practical engineering applications, an incipient warning and diagnosis method of sudden unbalance faults for rotating machinery is proposed. Characteristic indicators of historical data for normal operating conditions are extracted, and an incipient warning model based on support vector data description is built to detect incipient fault trends. Then, the mode identification is carried out applying reinforced fault diagnosis method including reinforced feature extraction, reinforced feature transfer, and reinforced pattern recognition three steps. Two groups actual engineering failure cases of petrochemical enterprises are used as test data, the incipient warning method is 2 400 min and 4 330 min earlier than the traditional vibration peak-to-peak alarm, respectively. The identification accuracy of the reinforced fault diagnosis method for two groups test data is 95% and 90%, respectively. Compared with other fault identification methods, the diagnosis accuracy and F-Score are the highest, and the standard deviation is the lowest. The results indicate that the proposed method possesses more excellent incipient fault detection performance, good robustness to cross-domain data fault identification of different equipment and working conditions, and strong domain generalization ability.

Key words: rotating machinery, incipient warning, fault diagnosis, reinforced feature transfer, domain generalization

CLC Number:

TH17

XIAO Yang, WANG Qingfeng, YANG Zhe, XU Wei, SHU Yue, CHEN Wenwu. Research on Incipient Warning and Diagnosis Method of Sudden Unbalance Fault for Rotating Machinery[J]. Journal of Mechanical Engineering, 2023, 59(11): 308-318.

References

[1] 冯志鹏，宋希庚，薛冬新，等. 旋转机械振动故障诊断理论与技术进展综述[J]. 振动与冲击，2001，20(4):36-39. FENG Zhipeng，SONG Xigeng，XUE Dongxin，et al. Survey of vibration fault diagnosis of rotational machinery[J]. Journal of Vibration and Shock，2001，20(4):36-39.
[2] GANGSAR P，PANDEY R K，CHOUKSEY M. Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features[J]. Noise & Vibration Worldwide，2021，52(4-5):75-85.
[3] CHU Zhigang，HUANG Di，YANG Liang. Field balancing of vehicle transmission shaft based on the influence coefficient method[J]. Noise & Vibration Worldwide，2018，49(7):266-271.
[4] 杨涛，任永，刘霞，等. 风力机叶轮质量不平衡故障建模及仿真研究[J]. 机械工程学报，2012，48(6):130-135. YANG Tao，REN Yong，LIU Xia，et al. Research on the modeling and simulation of wind turbine rotor imbalance fault[J]. Journal of Mechanical Engineering，2012，48(6):130-135.
[5] 李辉，杨东，杨超，等. 基于定子电流特征分析的双馈风电机组叶轮不平衡故障诊断[J]. 电力系统自动化，2015，39(13):32-37. LI Hui，YANG Dong，YANG Chao，et al. Blade imbalance fault diagnosis of doubly fed wind turbines based on stator current feature analysis[J]. Automation of Electric Power Systems，2015，39(13):32-37.
[6] 陈立芳，李兆举，王维民，等. 旋转机械不平衡振动自愈调控原理与方法[J]. 机械工程学报，2021，57(22):416-424. CHEN Lifang，LI Zhaoju，WANG Weimin， et al. Self-recovery principle and method for unbalanced vibration of rotating machinery[J]. Journal of Mechanical Engineering，2021，57(22):416-424.
[7] 王崇宇，郑召利，刘天源，等. 基于卷积神经网络的汽轮机转子不平衡与不对中故障检测方法研究[J]. 中国电机工程学报，2021，41(7):2417-2426. WANG Chongyu，ZHENG Zhaoli，LIU Tianyuan，et al. Research on detection method of steam turbine rotor unbalance and misalignment fault based on convolution neural network[J]. Proceedings of the CSEE，2021，41(7):2417-2426.
[8] 郑怀亮，王日新，杨远涛，等. 数据驱动故障诊断方法泛化性能的经验性分析[J]. 机械工程学报，2020，56(9):102-117. ZHENG Huailiang，WANG Rixin，YANG Yuantao，et al. An empirical analysis about the generalization performance of data-driven fault diagnosis methods[J]. Journal of Mechanical Engineering，2020，56(9):102-117.
[9] 刘阳，李诚，李富才，等. 航空发动机叶片脱落的非线性瞬态动力学研究[J]. 机械工程学报，2019，55(13):23-37. LIU Yang，LI Cheng，LI Fucai，et al. Non-linear transient rotor-dynamics analysis of fan blade out[J]. Journal of Mechanical Engineering，2019，55(13):23-37.
[10] 雷亚国，贾峰，孔德同，等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报，2018，54(4):94-104. LEI Yaguo，JIA Feng，KONG Detong，et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering，2018，54(4):94-104.
[11] 何正嘉，曹宏瑞，訾艳阳，等. 机械设备运行可靠性评估的发展与思考[J]. 机械工程学报，2014，50(2):171-186. HE Zhengjia，CAO Hongrui，ZI Yanyang，et al. Developments and thoughts on operational reliability assessment of mechanical equipment[J]. Journal of Mechanical Engineering，2014，50(2):171-186.
[12] AZAMI H，ROSTAGHI M，ABÁSOLO D，et al. Refined composite multiscale dispersion entropy and its application to biomedical signals[J]. IEEE Transactions on Biomedical Engineering，2017，64(12):2872-2879.
[13] WANG Qingfeng，LIU Xiaojin，WEI Bingkun，et al. Online incipient fault detection method based on improved l1 trend filtering and support vector data description[J]. IEEE Access，2021，9:30043-30059.
[14] TAX D M J，DUIN R P W. Support vector data description[J]. Machine Learning，2004，54(1):45-66.
[15] DRAGOMIRETSKIY K，ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing，2014，62(3):531-544.
[16] PAN S J，TSANG I W，KWOK J T，et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks，2011，22(2):199-210.
[17] ZHAO Zhibin，WU Jingyao，LI Tianfu，et al. Challenges and opportunities of AI-enabled monitoring，diagnosis & prognosis:a review[J]. Chinese Journal of Mechanical Engineering，2021，34(1):56.
[18] XUE Jiankai，SHEN Bo. A novel swarm intelligence optimization approach:Sparrow search algorithm[J]. Systems Science & Control Engineering，2020，8(1):22-34.
[19] 王庆锋，刘家赫，刘晓金，等. 数据驱动的旋转设备性能退化趋势预测方法[J]. 计算机集成制造系统，2022，28(3):724-734. WANG Qingfeng，LIU Jiahe，LIU Xiaojin，et al. Data-driven performance degradation trend predicting method for the rotating equipment[J]. Computer Integrated Manufacturing Systems，2022，28(3):724-734.
[20] SOKOLOVA M，JAPKOWICZ N，SZPAKOWICZ S. Beyond accuracy，f-score and roc:A family of discriminant measures for performance evaluation[C]// Australasian Joint Conference on Artificial Intelligence. AI 2006:Advances in Artificial Intelligence，December 4-8，2006，Berlin. Heidelberg:Springer-Verlag，2006:1015-1021.
[21] XU Wei，WAN Yi，ZUO Tianyu，et al. Transfer learning based data feature transfer for fault diagnosis[J]. IEEE Access，2020，8:76120-76129.
[22] 张西宁，余迪，刘书语. 基于迁移学习的小样本轴承故障诊断方法研究[J]. 西安交通大学学报，2021，55(10):30-37. ZHANG Xining，YU Di，LIU Shuyu. Fault diagnosis method for small sample bearing based on transfer learning[J]. Journal of Xi’an Jiaotong University，2021，55(10):30-37.
[23] 张倩，李海港，李明，等. 基于多源动态TrAdaBoost的实例迁移学习方法[J]. 中国矿业大学学报，2014，43(4):713-720. ZHANG Qian，LI Haigang，LI Ming，et al. Instance-based transfer learning method using multi-source dynamic TrAdaBoost[J]. Journal of China University of Mining & Technology，2014，43(4):713-720.
[24] 祝小彦，王永杰. 基于MOMEDA与Teager能量算子的滚动轴承故障诊断[J]. 振动与冲击，2018，37(6):104-123. ZHU Xiaoyan，WANG Yongjie. Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator[J]. Journal of Vibration and Shock，2018，37(6):104-123.
[25] GONG Boqing，SHI Yuan，SHA Fei，et al. Geodesic flow kernel for unsupervised domain adaptation[C]// IEEE Conference on Computer Vision and Pattern Recognition，June 16-21，2012，Providence. RI:IEEE，2012:2066-2073.