Volume 21 Issue 6
Dec.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(6): 269-277
ZHAO Zhi-hong, LI Qing, LI Le-hao, ZHAO Jing-jiao. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 269-277. doi: 10.19818/j.cnki.1671-1637.2021.06.021
Citation: ZHAO Zhi-hong, LI Qing, LI Le-hao, ZHAO Jing-jiao. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 269-277. doi: 10.19818/j.cnki.1671-1637.2021.06.021
shu

Remaining useful life prediction for equipment based on LSTM encoder-decoder method

doi: 10.19818/j.cnki.1671-1637.2021.06.021
  • 1.

    School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China

  • 2.

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China

Funds:

National Natural Science Foundation of China 11972236

National Natural Science Foundation of China 11790282

Graduate Innovation Support Project of Shijiazhuang Tiedao University YC2021077

More Information
  • Author Bio:

    ZHAO Zhi-hong(1972-), male, professor, PhD, hb_zhaozhihong@126.com

  • Received Date: 2021-05-20
    Available Online: 2022-02-11
  • Publish Date: 2021-12-01
    Abstract
  • A remaining useful life (RUL) prediction model of mechanical equipment was established based on the long short-term memory (LSTM) encoder-decoder method. The acquired sensor data were preprocessed. The data sequence was coded using the LSTM encoder method. An intermediate representation of the equipment status information was obtained. The characteristic information of the equipment status was obtained in the intermediate representation of the equipment status information. The intermediate representation information was decoded using the LSTM decoder method, and the RUL was predicted using the decoded information. RUL prediction experiments of the LSTM encoder-decoder method on open C-MAPSS data sets were performed. The LSTM encoder-decoder method was compared with the LSTM method, deep-LSTM (D-LSTM) method, and other methods. The effect of the sliding window size on RUL prediction results was evaluated. Research results show that scoring function values and root mean square error (RMSE) evaluation indexes of the RUL prediction results of the LSTM encoder-decoder method are more accurate than those of the LSTM method and D-LSTM method. In the FD001 subset, the RMSEs of the LSTM encoder-decoder method, LSTM method, and D-LSTM method are 11, 12, and 16, respectively. When the sliding window size is 30, the scoring function values corresponding to the FD001-FD004 subsets of the LSTM encoder-decoder method are 164, 3 012, 372, and 4 800, and the corresponding RMSEs are 11, 20, 14, and 22. When the sliding window size increases to 40, the respective scoring function values are 305, 1 220, 408, and 4 828, and the corresponding RMSEs are 14, 16, 15, and 19. Therefore, the proposed method based on the LSTM encoder-decoder effectively predicts the RUL of mechanical equipment, and the sliding window size significantly influences the RUL prediction results. 4 tabs, 6 figs, 32 refs.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    KHAN S, YAIRI T. A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing, 2018, 107: 241-265. doi: 10.1016/j.ymssp.2017.11.024
    [2]
    年夫顺. 关于故障预测与健康管理技术的几点认识[J]. 仪器仪表学报, 2018, 39(8): 1-14. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201808001.htm

    NIAN Fu-shun. Viewpoints about the prognostic and health management[J]. Chinese Journal of Scientific Instrument, 2018, 39(8): 1-14. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201808001.htm
    [3]
    ZHAO Ze-qi, LIANG Bin, WANG Xue-qian, et al. Remaining useful life prediction of aircraft engine based on degradation pattern learning[J]. Reliability Engineering and System Safety, 2017, 164: 74-83. doi: 10.1016/j.ress.2017.02.007
    [4]
    LIU Hui, LIU Zhen-yu, JIA Wei-qiang, et al. A novel deep learning-based encoder-decoder model for remaining useful life prediction[C]//IEEE. Proceedings of the 2019 International Joint Conference on Neural Networks. Washington DC: IEEE, 2019: 1-8.
    [5]
    LIM P, GOH C K, TAN K C. A time window neuralnetwork based framework for remaining useful life estimation[C]//IEEE. Proceedings of the 2016 International Joint Conference on Neural Networks. Washington DC: IEEE, 2016: 1746-1753.
    [6]
    LIAO Lin-xia, KÖTTIG F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction[J]. IEEE Transactions on Reliability, 2014, 63(1): 191-207. doi: 10.1109/TR.2014.2299152
    [7]
    朱朔, 白瑞林, 吉峰. 改进CHSMM的滚动轴承剩余寿命预测方法[J]. 机械传动, 2018, 42(10): 46-52, 95. https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201810010.htm

    ZHU Shuo, BAI Rui-lin, JI Feng. Rolling bearing remaining useful life prognosis method based on improved CHSMM[J]. Journal of Mechanical Transmission, 2018, 42(10): 46-52, 95. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201810010.htm
    [8]
    LI C J, LEE H. Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics[J]. Mechanical Systems and Signal Processing, 2005, 19(4): 836-846. doi: 10.1016/j.ymssp.2004.06.007
    [9]
    FAN Jia-jie, YUNG K C, PECHT M. Physics-of-failure-basedprognostics and health management for high-power white light-emitting diode lighting[J]. IEEE Transactions on Device and Materials Reliability, 2011, 11(3): 407-416. doi: 10.1109/TDMR.2011.2157695
    [10]
    姜媛媛, 曾文文, 沈静静, 等. 基于凸优化-寿命参数退化机理模型的锂离子电池剩余使用寿命预测[J]. 电力系统及其自动化学报, 2019, 31(3): 23-28. https://www.cnki.com.cn/Article/CJFDTOTAL-DLZD201903004.htm

    JIANG Yuan-yuan, ZENG Wen-wen, SHEN Jing-jing, et al. Prediction of remaining useful life of lithium-ion battery based on convex optimization-life parameter degradation mechanism model[J]. Proceedings of the CSU-EPSA, 2019, 31(3): 23-28. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLZD201903004.htm
    [11]
    张继冬, 邹益胜, 邓佳林, 等. 基于全卷积层神经网络的轴承剩余寿命预测[J]. 中国机械工程, 2019, 30(18): 2231-2235. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201918014.htm

    ZHANG Ji-dong, ZOU Yi-sheng, DENG Jia-lin, et al. Bearing remaining life prediction based on full convolutional layer neural networks[J]. China Mechanical Engineering, 2019, 30(18): 2231-2235. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201918014.htm
    [12]
    陈自强. 基于LSTM网络的设备健康状况评估与剩余寿命预测方法的研究[D]. 合肥: 中国科学技术大学, 2019.

    CHEN Zi-qiang. Research on equipment health assessment and remaining useful life prediction method based on LSTM[D]. Hefei: University of Science and Technology of China, 2019. (in Chinese)
    [13]
    KONG Zheng-min, CUI Yan-de, XIA Zhou, et al. Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics[J]. Applied Sciences, 2019, DOI: 10.3390/app9194156.
    [14]
    葛阳, 郭兰中, 牛曙光, 等. 基于t-SNE和LSTM的旋转机械剩余寿命预测[J]. 振动与冲击, 2020, 39(7): 223-231, 273. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202007032.htm

    GE Yang, GUO Lan-zhong, NIU Shu-guang, et al. Prediction of remaining useful life based on t-SNE and LSTM for rotating machinery[J]. Journal of Vibration and Shock, 2020, 39(7): 223-231, 273. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202007032.htm
    [15]
    康守强, 周月, 王玉静, 等. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法[J]. 自动化学报, 2020, DOI:10.16383/j.aas.c190796.

    KANG Shou-qiang, ZHOU Yue, WANG Yu-jing, et al. RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM[J]. Acta Automatica Sinica, 2020, DOI:10.16383/j.aas.c190796.(in Chinese)
    [16]
    SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61: 85-117.
    [17]
    CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. Computer Science, 2014, DOI: 10.3115/v1/D14-1179.
    [18]
    ZHU Yong-hua, ZHANG Wei-lin, CHEN Yi-ha, et al. A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment[J]. EURASIP Journal on Wireless Communications and Networking, 2019(1): 1-18.
    [19]
    BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166. doi: 10.1109/72.279181
    [20]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735
    [21]
    CHEN Yuan-hang, PENG Gao-liang, ZHU Zhi-yu, et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction[J]. Applied Soft Computing Journal, 2019, DOI:https://doi.org/ 10.1016/j.asoc.2019.105919.
    [22]
    李敏, 李红娇, 陈杰. 差分隐私保护下的Adam优化算法研究[J]. 计算机应用与软件, 2020, 37(6): 253-258, 296. doi: 10.3969/j.issn.1000-386x.2020.06.044

    LI Min, LI Hong-jiao, CHEN Jie. Adam optimization algorithm based on differential privacy protection[J]. Computer Applications and Software, 2020, 37(6): 253-258, 296. (in Chinese) doi: 10.3969/j.issn.1000-386x.2020.06.044
    [23]
    GOU Peng-qi, YU Jian-jun. A nonlinear ANN equalizer with mini-batch gradient descent in 40Gbaud PAM-8 IM/DD system[J]. Optical Fiber Technology, 2018, 46: 113-117. doi: 10.1016/j.yofte.2018.09.015
    [24]
    李杰, 贾渊杰, 张志新, 等. 基于融合神经网络的航空发动机剩余寿命预测[J]. 推进技术, 2021, 42(8): 1725-1734. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJS202108007.htm

    LI Jie, JIA Yuan-jie, ZHANG Zhi-xin, et al. Remaining useful life prediction of aeroengine based on fusion neural network[J]. Journal of Propulsion Technology, 2021, 42(8): 1725-1734. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJJS202108007.htm
    [25]
    ZHAO Zhi-bin, WU Jing-yao, LI Tian-fu, et al. Challenges and opportunities of AI-enabled monitoring, diagnosis and prognosis: a review[J]. Chinese Journal of Mechanical Engineering, 2021, 34(3): 16-44.
    [26]
    SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//IEEE. 2008 International Conference on Prognostics and Health Management. Washington DC: IEEE, 2008: 1-9.
    [27]
    ZHENG Shuai, RISTOVSKI K, FARAHAT A, et al. Long short-term memory network for remaining useful life estimation[C]//IEEE. 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). Washington DC: IEEE, 2017: 88-95.
    [28]
    HSU C S, JIANG J R. Remaining useful life estimation using long short-term memory deep learning[C]//IEEE. International Conference on Applied System Invention. Washington DC: IEEE, 2018: 58-61.
    [29]
    ZHANG C, LIM P, QIN A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2306-2318. doi: 10.1109/TNNLS.2016.2582798
    [30]
    LIM P, GOH C K, TAN K C, A time window neural network based framework for remaining useful life estimation[C]//IEEE. International Joint Conference on Neural Networks. Washington DC: IEEE, 2016: 1746-1753.
    [31]
    BABU G S, ZHAO Pei-lin, LI Xiao-li. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//Springer. International Conference on Database Systems for Advanced Applications. Berlin: Springer, 2016: 214-228.
    [32]
    AL-DULAIMI A, ZABIHI S, ASIF A, et al. A multimodal and hybrid deep neural network model for remaining useful life estimation[J]. Computers in Industry, 2019, 108: 186-196. doi: 10.1016/j.compind.2019.02.004
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1009) PDF downloads(100) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return