Predicting the Remaining Useful Life of Hydraulic Gate Based on Multi-feature Information Fusion

YANG Tao; ZHANG Yu-qi; FU Chun-jian; ZHAO Hua-dong

doi:10.11988/ckyyb.20221098

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Journal of Changjiang River Scientific Research Institute ›› 2024, Vol. 41 ›› Issue (2) : 188-197. DOI: 10.11988/ckyyb.20221098

Hydraulic Structure And Material

Predicting the Remaining Useful Life of Hydraulic Gate Based on Multi-feature Information Fusion

YANG Tao^1,2, ZHANG Yu-qi^1,2, FU Chun-jian², ZHAO Hua-dong¹

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Abstract

Predicting the remaining useful life (RUL) holds great significance in ensuring the operational safety of complex structures. To enhance the accuracy of RUL prediction for hydraulic gates, we propose a multi-feature information fusion-based approach. Initially, we employ the gamma process to simulate the corrosion evolution of gates and analyze the corrosion-caused degradation of characteristic parameters, including stress, natural vibration frequency, and dry/wet modal shapes through numerical simulations. Subsequently, we screen the feature parameters considering monotonicity and discreteness. We construct a health index by fusing these features based on principal component analysis. To model the gate degradation process, we employ a non-linear Wiener process and utilize the particle filtering method to obtain RUL prediction results for the gate at different operating times. Finally, we validate the reliability and effectiveness of our proposed method through engineering examples and finite element simulations. Our results demonstrate that the fusion of multiple information sources enables a more comprehensive reflection of the gate’s degradation state. The root mean square error (RMSE) of the prediction accuracy evaluation index is 1.395 5, the mean absolute error (MAE) is 1.262 8, and the absolute error of variance (VAE) is 0.352 8, showcasing high accuracy. This method can serve as a basis for gate health management and safety assessment.

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hydraulic steel gate / information fusion / remaining useful life prediction / particle filter

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YANG Tao, ZHANG Yu-qi, FU Chun-jian, ZHAO Hua-dong. Predicting the Remaining Useful Life of Hydraulic Gate Based on Multi-feature Information Fusion[J]. Journal of Changjiang River Scientific Research Institute. 2024, 41(2): 188-197 https://doi.org/10.11988/ckyyb.20221098

References

[1] NIU L,YAN J,GAO D.Research on Corrosion and Defects of Hydraulic Metal Structures[J]. IOP Conference Series: Earth and Environmental Science,2020,455(1):012027.
[2] KUZ’MITSKII M L, KSENOFONTOV N M. Corrosive Wear of Gate and Valve Metal Structures at Navigation Locks and Assessment of Their Remaining Lifetime[J]. Power Technology and Engineering,2016,50(2):164-167.
[3] 李伟康,王畅,张博越.基于D-H曲线及性能退化的钢闸门寿命预测方法[J].水利水电技术,2019,50(7):87-91.(LI Wei-kang, WANG Chang, ZHANG Bo-yue. Life Prediction Method of Steel Gate Based on D-H Curve and Performance Degradation[J]. Water Resources and Hydropower Engineering, 2019, 50(7): 87-91.(in Chinese))
[4] 方苇,陈梦成,张锐,等.基于时变Gamma随机过程的钢材锈蚀建模方法[J].建筑结构学报,2020,41(增刊2):382-388.(FANG Wei,CHEN Meng-cheng,ZHANG Rui,et al. Time-varying Gamma Stochastic Process-based Modeling Method for Steel Corrosion[J]. Journal of Building Structures,2020,41(S2):382-388.(in Chinese))
[5] 任子强, 司小胜, 胡昌华, 等. 融合多传感器数据的发动机剩余寿命预测方法[J]. 航空学报, 2019, 40(12): 134-145. (REN Zi-qiang, SI Xiao-sheng, HU Chang-hua, et al. Remaining Useful Life Prediction Method for Engine Combining Multi-sensors Data[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(12):134-145.(in Chinese))
[6] 赵广社, 吴思思, 荣海军. 多源统计数据驱动的航空发动机剩余寿命预测方法[J]. 西安交通大学学报, 2017, 51(11): 150-155, 172. (ZHAO Guang-she, WU Si-si, RONG Hai-jun. A Multi-source Statistics Data-driven Method for Remaining Useful Life Prediction of Aircraft Engine[J]. Journal of Xi’an Jiaotong University, 2017, 51(11): 150-155, 172.(in Chinese))
[7] 李乃鹏, 蔡潇, 雷亚国, 等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报, 2021, 57(20): 29-37, 46. (LI Nai-peng, CAI Xiao, LEI Ya-guo, et al. A Model-data-fusion Remaining Useful Life Prediction Method with Multi-sensor Fusion for Machinery[J]. Journal of Mechanical Engineering, 2021, 57(20): 29-37, 46.(in Chinese))
[8] 王艺玮, 邓蕾, 郑联语, 等. 基于多通道融合及贝叶斯理论的刀具剩余寿命预测方法[J]. 机械工程学报, 2021, 57(13): 214-224. (WANG Yi-wei, DENG Lei, ZHENG Lian-yu, et al. A Multi-channel Signal Fusion and Bayesian Theory Based Method for Tool Remaining Useful Life Prediction[J]. Journal of Mechanical Engineering, 2021, 57(13): 214-224.(in Chinese))
[9] LI N, LEI Y, YAN T, et al. A Wiener-process-model-based Method for Remaining Useful Life Prediction Considering Unit-to-unit Variability[J]. IEEE Transactions on Industrial Electronics, 2019, 66(3): 2092-2101.
[10]张英波,贾云献,冯添乐,等.基于Gamma退化过程的直升机主减速器行星架剩余寿命预测模型[J].振动与冲击,2012,31(14):47-51. (ZHANG Ying-bo, JIA Yun-xian, FENG Tian-le, et al. Remaining Useful Life Prediction Model of Planetary Carrier in Helicopter Main Gear-box Based on Gamma Degradation Process[J]. Journal of Vibration and Shock,2012,31(14):47-51.(in Chinese))
[11]李玥锌,刘淑杰,高斯博,等.基于维纳过程的锂离子电池剩余寿命预测[J].大连理工大学学报,2017,57(2):126-132. (LI Yue-xin, LIU Shu-jie, GAO Si-bo, et al. Prediction of Lithium-ion Battery’s Remaining Useful Life Based on Wiener Process[J]. Journal of Dalian University of Technology, 2017, 57(2): 126-132.(in Chinese))
[12]王玺,周薇,胡昌华,等.基于粒子滤波的非线性退化设备剩余寿命自适应预测[J].兵器装备工程学报,2020,41(10):41-47,57.(WANG Xi,ZHOU Wei,HU Chang-hua,et al. Adaptive Remaining Useful Life Prediction for Nonlinear Degradation Equipment Based on Particle Filter[J]. Journal of Ordnance Equipment Engineering,2020,41(10):41-47,57.(in Chinese))
[13]黄天立, 周浩, 王超, 等. 基于伽马过程的锈蚀钢筋混凝土桥梁检测维护策略优化[J]. 中南大学学报(自然科学版), 2015, 46(5): 1851-1861. (HUANG Tian-li, ZHOU Hao, WANG Chao, et al. Optimization Inspection and Maintenance Strategy for Corrosive Reinforced Concrete Girder Bridges Based on Gamma Process[J]. Journal of Central South University (Science and Technology), 2015, 46(5): 1851-1861.(in Chinese))
[14]MAHMOODIAN M, ALANI A. Modeling Deterioration in Concrete Pipes as a Stochastic Gamma Process for Time-dependent Reliability Analysis[J]. Journal of Pipeline Systems Engineering and Practice,2014,5(1):577-605.
[15]张冬梅,陈淙岑.管片钢筋和螺栓锈蚀条件下盾构隧道结构时变可靠度分析[J].现代隧道技术,2021,58(6):111-120. (ZHANG Dong-mei, CHEN Cong-cen. Time-dependent Reliability Analysis of Shield Tunnel Structure under Corrosion Conditions of Segment Rebars and Bolts[J]. Modern Tunnelling Technology, 2021, 58(6): 111-120.(in Chinese))
[16]徐卫敏,卢鹏程,范兴朗.基于Gamma随机过程的水工钢闸门时变可靠度方法[J]. 浙江建筑,2018,35(6):32-35.(XU Wei-min,LU Peng-cheng,FAN Xing-lang.Method for Time-dependent Reliability Analysis of Hydraulic Steel Gates Based on Gamma Stochastic Process[J]. Zhejiang Construction, 2018, 35(6): 32-35.(in Chinese))
[17]孙曙光,唐尧,王景芹,等.基于多信号特征融合的断路器操作机构寿命预测[J].高电压技术,2022,48(11):4455-4468. (SUN Shu-guang, TANG Yao, WANG Jing-qin, et al. Life Prediction of Operating Mechanism for Circuit Breaker Based on Multiple Signal Feature Fusion[J]. High Voltage Engineering, 2022, 48(11): 4455-4468.(in Chinese))
[18]AN D, CHOI J H, KIM N H. Prognostics 101: A Tutorial for Particle Filter-based Prognostics Algorithm Using MatLab[J]. Reliability Engineering & System Safety, 2013, 115: 161-169.
[19]文娟, 高宏力. 一种基于UPF的轴承剩余寿命预测方法[J]. 振动与冲击, 2018, 37(24): 208-213, 243. (WEN Juan, GAO Hong-li. Remaining Useful Life Prediction of Bearings with the Unscented Particle Filter Approach[J]. Journal of Vibration and Shock, 2018, 37(24): 208-213, 243.(in Chinese))
[20]周建方,李典庆,李朝晖,等.钢闸门结构时变抗力模型及其可靠度分析[J].工程力学,2003,20(4):104-109. (ZHOU Jian-fang, LI Dian-qing, LI Zhao-hui, et al. Time-variant Resistance Model and Reliability Analysis of Steel Gate Structures[J]. Engineering Mechanics, 2003, 20(4): 104-109.(in Chinese))
[21]SL 74—2013, 水利水电工程钢闸门设计规范[S]. 北京: 中国标准出版社, 2013. (SL 74—2013, Hydraulic and Hydroelectric Engineering Specification for Design of Steel Gate[S]. Beijing: Standards Press of China, 2013.(in Chinese))
[22]WEN J, GAO H, ZHANG J. Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model[J]. Shock and Vibration, 2018, Doi: 10.1155/2018/4068431.