Soft Measurement Method for Health State of Typical Complex Feeding and Ramming Mechanism Based on Time-frequencyData Drive

ZHANG Gang;LIANG Weige;SHE Bo;TIAN Fuqing

CN Submission Journal Articles Figures Search
Adv. Search

  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2022, Vol. 43 ›› Issue (4) : 737-747. DOI: 10.12382/bgxb.2021.0068
Paper

Soft Measurement Method for Health State of Typical Complex Feeding and Ramming Mechanism Based on Time-frequencyData Drive

  • ZHANG Gang1,2, LIANG Weige1, SHE Bo1, TIAN Fuqing1
Author information +
History +

Abstract

The operating environment of complex feeding and ramming mechanisms is harsh, and the vibration signals collected by sensors contain violent impact, noise and other components, which are typical non-stationary characteristic signals, and the health state of feeding and ramming mechanisms is difficultly evaluated. To solve these problems, a soft measurement method based on time-frequency data driving is proposed for measuring the health state of feeding and ramming mechanism. The time-frequency graph of vibration acceleration signal, as an input feature, is obtained by Morlet wavelet transform, and a soft measurement model based on deep convolutional network isestablished. The Dropout regularization term is introduced into the deep convolutional network to relieve overfitting phenomenon, and the uncertainty of soft measurement results is analyzed quantitatively. The bench test of complex feeding and ramming mechanism shows that the proposed soft measurement method can effectively distinguish the health state of feeding and ramming mechanism with the accuracy of 90%. In the stage of performance degradation, the degradation degree of the mechanism performance can be quantitatively analyzed, and the measured error is about 7%. Compared with other data-driven soft measurement methods for health state, the proposed method can effectively improve the identification accuracy of health state and the measuring accuracy of performance degradation of feeding and ramming mechanisms, and reduce the uncertainty of measureed results.

Key words

complexfeedingandrammingmechanism / healthstate / softmeasurement / time-frequencydata

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
ZHANG Gang, LIANG Weige, SHE Bo, TIAN Fuqing. Soft Measurement Method for Health State of Typical Complex Feeding and Ramming Mechanism Based on Time-frequencyData Drive. Acta Armamentarii. 2022, 43(4): 737-747 https://doi.org/10.12382/bgxb.2021.0068

References


［1］舒长胜, 孟庆德. 舰炮武器系统应用工程基础［M］. 北京: 国防工业出版社, 2014.
SHU C S, MENG Q D. Fundamentals of applied engineering of naval gun weapon system ［M］. Beijing: National Defense Industry Press, 2014. (in Chinese)
［2］杨丽. 自动火炮供弹机可靠性及关键性能评估策略研究［D］. 沈阳: 东北大学, 2015.
YANG L. Research on reliability of automatic cannon feeding mechanism and assessment strategy of its key performance ［D］. Shenyang: Northeastern University, 2015.(in Chinese)
［3］PARISP C, ERDOGAN F. A critical analysis of crack propagation laws［J］. Journal of Fluids Engineering, 1963, 85(4):528-533.
［4］CHAN K S, ENRIGHT M P, MOODY J P, et al. Life prediction for turbopropulsion systems under dwell fatigue conditions［J］. Journal of Engineering for Gas Turbines and Power, 2012, 134(12):122501.
［5］彭志凌, 张毅, 丁明军, 等. 某供弹系统高速传动机构磨损机理分析与预测模型［J］. 中北大学学报（自然科学版）, 2018, 39(2): 155-158.
PENG Z L, ZHANG Y, DING M J, et al. Analysis and prediction model of wear mechanism for high speed transmission mechanism of a missile system［J］. Journal of North University of China(Natural Science Edition), 2018, 39(2): 155-158.(in Chinese)
［6］WANG J J, GAO R X, YUAN Z, et al. A joint particle filter and expectation maximization approach to machine condition prognosis［J］. Journal of Intelligent Manufacturing, 2019, 30(2): 605-621.
［7］LEIY, LI N, GONTARZ S, et al. A model-based method for remaining useful life prediction of machinery［J］. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326
［8］LIAOL. Discovering prognostic features using genetic programming in remaining useful life prediction［J］. IEEE Transactions on Industrial Electronics, 2014, 61(5): 2464-2472.
［9］MARCIA L B, ELSA M P H, KAI G. More effective prognostics with elbow point detection and deep learning［J］. Mechanical Systems and Signal Processing, 2021, 146: 106987.
［10］MENGM, ZHU M. Deep-convolution-based LSTM network for remining useful life prediction［J］. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1658-1667.
［11］SHE D M, JIA M P. A BiGRU method for remaining useful life prediction of machinery［J］. Measurement, 2021, 167: 108277.
［12］汤健, 田福庆, 贾美英, 等. 基于频谱数据驱动的旋转机械设备负荷软测量［M］. 北京: 国防工业出版社, 2015.
TANG J, TIAN F Q, JIA M Y, et al. Soft measurement of rotating machinery equipment driven by spectral data［M］. Beijing: National Defense Industry Press, 2015. (in Chinese)
［13］PAN Y B, HONG R J, CHEN J, et al. A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox［J］. Renewable Energy, 2020, 152: 138-154.
［14］朱林, 陈敏, 贾民平. 基于贝叶斯理论的结构件健康状态评估方法研究［J］. 振动与冲击, 2020, 39(6): 59-63.
ZHU L, CHEN M, JIA M P. Approach for structural health assessment based on the Bayesian theory［J］. Journal of Vibration and Shock, 2020, 39(6):59-63.(in Chinese)
［15］彭宅铭, 程龙生, 詹君, 等. 基于改进马田系统的复杂系统健康状态评估［J］. 系统工程与电子技术, 2020, 42(4): 960-968.
PENG Z M, CHENG L S, ZHAN J, et al. Health status assessment for complex system based on improved Mahalanobis-Taguchi system［J］. Systems Engineering and Electronics, 2020, 42(4): 960-968.(in Chinese)
［16］潘宏侠, 张玉学. 基于SST时频图纹理特征的供输弹系统故障诊断［J］. 振动与冲击, 2020, 39(6): 132-137.
PAN H X, ZHANG Y X. Fault diagnosis of the ammunition supply system based on the texture features of SST time-frequency distribution image［J］. Journal of Vibration and Shock, 2020, 39(6):132-137.(in Chinese)
［17］张航, 潘宏侠, 许昕, 等. 基于MRSVD与灰色理论的供输弹系统故障诊断研究［J］. 中国测试, 2019, 45(7): 147-151.
ZHANG H, PAN H X, XU X, et al. Study on the fault diagnosis of ammunition supply system based on MRSVD and grey theory［J］. China Measurement and Test, 2019, 45(7): 147-151.(in Chinese)
［18］席茂松, 许昕, 潘宏侠, 等. 基于多尺度特征融合的供输弹早期故障诊断［J］. 机械设计与研究, 2020, 36(4): 216-219.
XI M S, XU X, PAN H X, et al. Early fault diagnosis of ammunition supply and ramming based on multi-scale feature fusion［J］. Machine Design and Research, 2020, 36(4): 216-219.(in Chinese)
［19］周飞燕, 金林鹏, 董军. 卷积神经网络研究综述［J］. 计算机学报, 2017, 40（6）: 1229-1251.
ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network［J］. Chinese Journal of Computers, 2017,40(6): 1229-1251.(in Chinese)
［20］IANG, YOSHUA B, AARON C. 深度学习［M］. 赵申剑，译. 北京：人民邮电出版社，2017.
IAN G, YOSHUA B, AARON C. Deep learning［M］. ZHAO S J, translated. Beijing: Post and Telecom Press, 2017. (in Chinese)
［21］陈仁祥, 张勇, 杨黎霞, 等. 基于整周期数据和卷积神经网络的谐波减速器健康状态评估［J］. 仪器仪表学报, 2020, 41(2): 245-252.
CHEN R X, ZHANG Y, YANG L X, et al. Health condition assessment of harmonic reducer based on integer-period data and convolution neural network［J］. Chinses Journal of Scientific Instrument, 2020, 41(2): 245-252.(in Chinese)


224

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉