Data-model Interactive Remaining Useful Life Prediction Technologies for Stochastic Degrading Devices With Big Data
-
摘要: 面向大数据背景下随机退化设备剩余寿命(Remaining useful life, RUL)预测的现实需求, 结合随机退化设备监测大数据特点及剩余寿命预测不确定性量化这一核心问题, 深入分析了机理模型与数据混合驱动的剩余寿命预测技术、基于机器学习的剩余寿命预测技术、统计数据驱动的剩余寿命预测技术以及机器学习和统计数据驱动相结合的剩余寿命预测技术的基本研究思想和发展动态, 剖析了当前研究存在的局限性和共性难题. 针对存在的局限性和共性难题, 以多源传感监测大数据下剩余寿命预测问题为例, 提出了一种数模联动的大数据下随机退化设备剩余寿命预测解决思路, 并通过航空发动机多源监测数据初步验证了该思路的可行性和有效性. 最后, 借鉴数模联动思路, 综合考虑机器学习方法和统计数据驱动方法的优势, 紧紧扭住大数据背景下随机退化设备剩余寿命预测不确定性量化问题, 提出了大数据背景下深度学习与随机退化建模交互联动、监测大数据与剩余寿命及其预测不确定性映射机制、非理想大数据下的剩余寿命预测等亟待解决的关键科学问题.Abstract: Focused on the realistic desire to the remaining useful life (RUL) prediction of stochastic degrading devices with big data, according to the characteristics of the big monitoring data of stochastic degrading devices and the core issue quantifying the uncertainty in the RUL prediction, this paper provides deep analysis of basic principles and advances of classical solution avenues to RUL prediction of stochastic degrading devices with big data from the data-driven viewpoint. The reviewed methods mainly include hybrid techniques based on physical model and data, machine learning method based techniques, statistical data-driven techniques, and the combination of machine learning methods and statistical data-driven methods. At the meanwhile, the limitations and common problems in existing studies are dissected. As for these limitations, taking the big monitoring data from multi-source sensors as an example, this paper presents a data-model interaction perspective to solve the RUL prediction problem for stochastic degrading devices with big data. The application to multi-source monitoring data of aero-engines preliminarily verifies the feasibility and effectiveness of this presented data-model interaction idea. Finally, inspired by the presented data-model interaction idea, it will be beneficial to tightly holding the main line of the RUL prediction uncertainty quantification by synthesizing advantages of intelligent methods and statistical data driven methods. As such, this paper discusses several key scientific issues for RUL prediction of stochastic degrading devices with big data, including the interactive collaboration idea between deep learning and stochastic degradation modeling, the mapping mechanism between the big monitoring data and the RUL with the prediction uncertainty quantification, RUL prediction issues under non-ideal data, etc.
-
图 3 多源传感器剩余寿命预测数模联动解决方案与流程图
Fig. 3 Idea and flowchart of data-model interactive remaining useful life prediction with multi-source sensors
表 1 皮尔逊相关系数对比结果
Table 1 Comparative results of Pearson correlation coefficients
健康指标 皮尔逊相关系数 单一传感器 低压压气机出口总温度 (T24) 0.6753 高压压气机出口总温度 (T30) 0.6440 低压涡轮出口总温度 (T50) 0.7816 高压压气机出口总压力 (P30) −0.7615 高压压气机出口静压 (Ps30) 0.8106 燃料流量与Ps30的比率 (phi) −0.7897 旁路比率 (BRP) 0.7248 出血焓 (htBleed) 0.6731 高压涡轮冷却剂排放 (W31) −0.7141 低压涡轮冷却剂排放 (W32) −0.7167 本文数模联动复合健康指标 0.9002 
下载: 导出CSV
表 2 失效时刻健康指标值的方差比较
Table 2 Variance of health indices at failure time
健康指标 方差值 单一传感器 低压压气机出口总温度 (T24) 0.0274 高压压气机出口总温度 (T30) 0.0176 低压涡轮出口总温度 (T50) 0.0140 高压压气机出口总压力 (P30) 0.0264 高压压气机出口静压 (Ps30) 0.0154 燃料流量与 Ps30 的比率 (phi) 0.0206 旁路比率 (BRP) 0.0225 出血焓 (htBleed) 0.0435 高压涡轮冷却剂排放 (W31) 0.0220 低压涡轮冷却剂排放 (W32) 0.0317 复合健康指标 [133] 0.0035 复合健康指标 [137] 0.0101 本文数模联动复合健康指标 0.0013
下载: 导出CSV
表 3 剩余寿命预测性能比较
Table 3 Comparative results in the performance of the remaining useful life prediction
预测方法 Score Accuracy (%) MSE 支持向量回归方法[65] 449 70 — 基于案例的学习方法[71] 1389.26 44 — 基于案例的推理方法[72] 216 67 176 多目标深度置信网络集成方法[86] 334.23 — 226.20 卷积神经网络[92] 1287 — 340 循环神经网络[96] 219 59 155 受限玻尔兹曼机 + LSTM网络[104] 231 — 157.75 基于长短时网络的编码−解码器[106] 256 67 164 循环神经网络 + 自编码器[107] 245 70 — 基于多损失编码器与卷积复合特征的两阶段深度学习方法[108] 208 — 133.86 深度置信网络 + 后向传播神经网络 + 改进粒子滤波算法[140] 543 51 283 深度置信网络 + 改进粒子滤波算法[140] 314 63 172 线性Wiener随机过程方法 低压压气机出口总温度 (T24) $1.32 \times 10 ^9$ 45 1193.76 高压压气机出口总温度 (T30) $2.96 \times 10 ^7$ 32 1288.29 低压涡轮出口总温度 (T50) 377.67 62 210.86 高压压气机出口总压力 (P30) 5109.67 53 420.48 高压压气机出口静压 (Ps30) 1328.63 61 296.03 燃料流量与 Ps30 的比率 (phi) 1442.09 57 325.20 旁路比率 (BRP) $2.59 \times 10 ^4$ 48 501.06 出血焓 (htBleed) 2847.74 30 669.43 高压涡轮冷却剂排放 (W31) $4.92 \times 10 ^4$ 48 458.40 低压涡轮冷却剂排放 (W32) 1564.21 46 427.19 本文数模联动预测方法 95.87 81 68.29 注: 表中“—”表示原文中没有计算并给出该指标值.
下载: 导出CSV
-
[1] Pecht M G. Prognostics and Health Management of Electronics. Hoboken: Wiley, 2008. [2] Si X S, Wang W B, Hu C H, Zhou D H. Remaining useful life estimation—a review on the statistical data driven approaches. European Journal of Operational Research, 2011, 213(1): 1-14 doi: 10.1016/j.ejor.2010.11.018 [3] 彭宇, 刘大同. 数据驱动故障预测和健康管理综述. 仪器仪表学报, 2014, 35(3): 481-495Peng Yu, Liu Da-Tong. Data-driven prognostics and health management: A review of recent advances. Chinese Journal of Scientific Instrument, 2014, 35(3): 481-495 [4] Liao L X, Köttig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 2014, 63(1): 191-207 doi: 10.1109/TR.2014.2299152 [5] 喻勇, 司小胜, 胡昌华, 崔忠马, 李洪鹏. 数据驱动的可靠性评估与寿命预测研究进展: 基于协变量的方法. 自动化学报, 2018, 44(2): 216-227Yu Yong, Si Xiao-Sheng, Hu Chang-Hua, Cui Zhong-Ma, Li Hong-Peng. Data driven reliability assessment and life-time prognostics: A review on covariate models. Acta Automatica Sinica, 2018, 44(2): 216-227 [6] 施权, 胡昌华, 司小胜, 扈晓翔, 张正新. 考虑执行器性能退化的控制系统剩余寿命预测方法. 自动化学报, 2019, 45(5): 941-952Shi Quan, Hu Chang-Hua, Si Xiao-Sheng, Hu Xiao-Xiang, Zhang Zheng-Xin. Remaining useful lifetime prediction method of controlled systems considering performance degradation of actuator. Acta Automatica Sinica, 2019, 45(5): 941-952 [7] 陆宁云, 陈闯, 姜斌, 邢尹. 复杂系统维护策略最新研究进展: 从视情维护到预测性维护. 自动化学报, 2021, 47(1): 1-17Lu Ning-Yun, Chen Chuang, Jiang Bin, Xing Yin. Latest progress on maintenance strategy of complex system: From condition-based maintenance to predictive maintenance. Acta Automatica Sinica, 2021, 47(1): 1-17 [8] 袁烨, 张永, 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状. 自动化学报, 2020, 46(10): 2013-2030Yuan Ye, Zhang Yong, Ding Han. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Automatica Sinica, 2020, 46(10): 2013-2030 [9] van Asselt M B A, Mesman J, van’t Klooster S A. Dealing with prognostic uncertainty. Futures, 2007, 39(6): 669-684 doi: 10.1016/j.futures.2006.11.011 [10] Hess A, Calvello G, Frith P, Engel S J, Hoitsma D. Challenges, issues, and lessons learned chasing the “Big P”: Real predictive prognostics Part 2. In: Proceedings of the 2006 IEEE Aerospace Conference. Big Sky, USA: IEEE, 2006. [11] Smith G, Schroeder J B, Navarro S, Haldeman D. Development of a prognostics and health management capability for the joint strike fighter. In: Proceedings of the 1997 IEEE Autotestcon Proceedings Autotestcon＇97. IEEE Systems Readiness Technology Conference. Systems Readiness Supporting Global Needs and Awareness in the 21st Century. Anaheim, USA: IEEE, 1997. [12] Pecht M, Jaai R. A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability, 2010, 50(3): 317-323 doi: 10.1016/j.microrel.2010.01.006 [13] Brombacher A. Reliability prediction and ‘Deepwater Horizon’; lessons learned. Quality and Reliability Engineering International, 2010, 26(5): Article No. 397 doi: 10.1002/qre.1135 [14] Si X S, Li T M, Zhang Q, Hu C H. Prognostics for linear stochastic degrading systems with survival measurements. IEEE Transactions on Industrial Electronics, 2020, 67(4): 3202-3215 doi: 10.1109/TIE.2019.2908617 [15] Chen J L, Jing H J, Chang Y H, Liu Q. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 2019, 185: 372-382 [16] Kundu P, Darpe A K, Kulkarni M S. Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions. Mechanical Systems and Signal Processing, 2019, 143: Article No. 106302 [17] Qian Y N, Yan R Q, Hu S J. Bearing degradation evaluation using recurrence quantification analysis and Kalman filter. IEEE Transactions on Instrumentation and Measurement, 2014, 63(11): 2599-2610 doi: 10.1109/TIM.2014.2313034 [18] Jin X H, Sun Y, Que Z J, Wang Y, Chow T W S. Anomaly detection and fault prognosis for bearings. IEEE Transactions on Instrumentation and Measurement, 2016, 65(9): 2046-2054 doi: 10.1109/TIM.2016.2570398 [19] Singleton R K, Strangas E G, Aviyente S. Extended Kalman filtering for remaining-useful-life estimation of bearings. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1781-1790 doi: 10.1109/TIE.2014.2336616 [20] Liao L X. Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Transactions on Industrial Electronics, 2014, 61(5): 2464-2472 doi: 10.1109/TIE.2013.2270212 [21] Li N P, Lei Y G, Lin J, Ding S X. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773 doi: 10.1109/TIE.2015.2455055 [22] Choi J H, An D, Gang J, Joo J, Kim N H. Bayesian approach for parameter estimation in the structural analysis and prognosis. In: Proceedings of the 2010 Annual Conference of the Prognostics and Health Management Society. Portland, USA: IEEE, 2010. [23] An D, Choi J H. Improved MCMC method for parameter estimation based on marginal probability density function. Journal of Mechanical Science and Technology, 2013, 27(6): 1771-1779 doi: 10.1007/s12206-013-0428-9 [24] Paris P, Erdogan F. A critical analysis of crack propagation laws. Journal of Basic Engineering, 1963, 85(4): 528-533 doi: 10.1115/1.3656900 [25] Forman R G. Study of fatigue crack initiation from flaws using fracture mechanics theory. Engineering Fracture Mechanics, 1972, 4(2): 333-345 doi: 10.1016/0013-7944(72)90048-3 [26] Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, Liang S. Adaptive prognostics for rolling element bearing condition. Mechanical Systems and Signal Processing, 1999, 13(1): 103-113 doi: 10.1006/mssp.1998.0183 [27] Li Y, Kurfess T R, Liang S Y. Stochastic prognostics for rolling element bearings. Mechanical Systems and Signal Processing, 2000, 14(5): 747-762 doi: 10.1006/mssp.2000.1301 [28] Li C J, Lee H. Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 2005, 19(4): 836-846 doi: 10.1016/j.ymssp.2004.06.007 [29] Liang S Y, Li Y W, Billington S A, Zhang C, Shiroishi J, Kurfess T R, et al. Adaptive prognostics for rotary machineries. Procedia Engineering, 2014, 86: 852-857 doi: 10.1016/j.proeng.2014.11.106 [30] Oppenheimer C H, Loparo K A. Physically based diagnosis and prognosis of cracked rotor shafts. In: Proceedings of the SPIE 4733, Component and Systems Diagnostics, Prognostics, and Health Management II. Orlando, USA: SPIE, 2002. 122−133 [31] Marble S, Morton B P. Predicting the remaining life of propulsion system bearings. In: Proceedings of the 2006 IEEE Aerospace Conference. Big Sky, USA: IEEE, 2006. [32] Choi Y, Liu C R. Spall progression life model for rolling contact verified by finish hard machined surfaces. Wear, 2007, 262(1-2): 24-35 doi: 10.1016/j.wear.2006.03.041 [33] Liao L X, Köttig F. A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing, 2016, 44: 191-199 doi: 10.1016/j.asoc.2016.03.013 [34] Wang B, Lei Y G, Li N P, Li N B. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 2020, 69(1): 401-412 doi: 10.1109/TR.2018.2882682 [35] Cheng S F, Pecht M. A fusion prognostics method for remaining useful life prediction of electronic products. In: Proceedings of the 2009 IEEE International Conference on Automation Science and Engineering. Bangalore, India: IEEE, 2009. 102−107 [36] Goebel K, Eklund N. Prognostic fusion for uncertainty reduction. In: Proceedings of the 2007 Conference and Exhibit. Rohnert Park, California, USA: AIAA, 2007. [37] Bartram G, Mahadevan S. Prognostics and health monitoring in the presence of heterogeneous information. In: Proceedings of the 2012 Annual Conference of the Prognostics and Health Management Society. Minneapolis, USA: IEEE, 2012. [38] 裴洪, 胡昌华, 司小胜, 张建勋, 庞哲楠, 张鹏. 基于机器学习的设备剩余寿命预测方法综述. 机械工程学报, 2019, 55(8): 1-13 doi: 10.3901/JME.2019.08.001Pei Hong, Hu Chang-Hua, Si Xiao-Sheng, Zhang Jian-Xun, Pang Zhe-Nan, Zhang Peng. A review of machine learning based remaining useful life prediction methods for equipment. Journal of Mechanical Engineering, 2019, 55(8): 1-13 doi: 10.3901/JME.2019.08.001 [39] Khan S, Yairi T. A review on the application of deep learning in system health Management. Mechanical Systems and Signal Processing, 2018, 107: 241-265 doi: 10.1016/j.ymssp.2017.11.024 [40] Kim D E, Gofman M. Comparison of shallow and deep neural networks for network intrusion detection. In: Proceedings of the 8th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas, USA: IEEE, 2018. 204−208 [41] 余凯, 贾磊, 陈雨强, 徐伟. 深度学习的昨天、今天和明天. 计算机研究与发展, 2013, 50(9): 1799-1804 doi: 10.7544/issn1000-1239.2013.20131180Yu Kai, Jia Lei, Chen Yu-Qiang, Xu Wei. Deep learning: Yesterday, today, and tomorrow. Journal of Computer Research and Development, 2013, 50(9): 1799-1804 doi: 10.7544/issn1000-1239.2013.20131180 [42] Bishop C M. Pattern Recognition and Machine Learning. New York: Springer, 2006. [43] Ali J B, Chebel-Morello B, Saidi L, Malinowski S, Fnaiech F. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 2015, 56-57: 150-172 doi: 10.1016/j.ymssp.2014.10.014 [44] 李世科. 基于LM-BP神经网络的液压支架顶梁疲劳寿命预测及应用. 中国矿业, 2019, 28(5): 92-96Li Shi-Ke. Fatigue life prediction and application of hydraulic support roof beam based on LM-BP neural network. China Mining Magazine, 2019, 28(5): 92-96 [45] 邱晓梅, 隋文涛, 王峰, 张洪波, 金亚军. 基于相关系数和BP神经网络的轴承剩余寿命预测. 组合机床与自动化加工技术, 2019(4): 63-65Qiu Xiao-Mei, Sui Wen-Tao, Wang Feng, Zhang Hong-Bo, Jin Ya-Jun. Remaining life prediction of bearing based on correlation coefficient and BP Neural Network. Modular Machine Tool & Automatic Manufacturing Technique, 2019(4): 63-65 [46] Gebraeel N, Lawley M, Liu R, Parmeshwaran V. Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics, 2004, 51(3): 694-700 doi: 10.1109/TIE.2004.824875 [47] Mahamad A K, Saon S, Hiyama T. Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics With Applications, 2010, 60(4): 1078-1087 [48] Lim P, Goh C K, Tan K C. A novel time series-histogram of features (TS-HoF) method for prognostic applications. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(3): 204-213 doi: 10.1109/TETCI.2018.2822836 [49] Drouillet C, Karandikar J, Nath C, Journeaux A C, El Mansori M, Kurfess T. Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes, 2016, 22: 161-168 doi: 10.1016/j.jmapro.2016.03.010 [50] Ahmadzadeh F, Lundberg J. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 2013, 53: 1-8 doi: 10.1016/j.mineng.2013.05.026 [51] Zhang Z Y, Wang Y, Wang K S. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 2013, 24(6): 1213-1227 doi: 10.1007/s10845-012-0657-2 [52] 徐东辉. 车用锂离子动力电池剩余寿命非线性组合预测研究. 北京师范大学学报(自然科学版), 2021, 57(5): 571-576Xu Dong-Hui. Nonlinear combination prediction of remaining useful life of automotive Lithium-ion batteries. Journal of Beijing Normal University (Natural Science), 2021, 57(5): 571-576 [53] 杨洋. 基于ARIMA和BP神经网络组合模型的锂电池寿命预测 [博士学位论文], 海南大学, 中国, 2020.Yang Yang. Battery Life Prediction Based on ARIMA with BPNN [Ph.D. dissertation], Hainan University, China, 2020. [54] Bektas O, Jones J A, Sankararaman S, Roychoudhury I, Goebel K. A neural network filtering approach for similarity-based remaining useful life estimation. The International Journal of Advanced Manufacturing Technology, 2019, 101(1-4): 87-103 doi: 10.1007/s00170-018-2874-0 [55] Li Z X, Wu D Z, Hu C, Terpenny J. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliability Engineering & System Safety, 2019, 184: 110-122 [56] Cortes C. Prediction of Generalization Ability in Learning Machines [Ph.D. dissertation], University of Rochester, USA, 1995. [57] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995. [58] Benkedjouh T, Medjaher K, Zerhouni N, Rechak S. Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 2015, 26(2): 213-223 doi: 10.1007/s10845-013-0774-6 [59] Liu J, Zio E. An adaptive online learning approach for support Vector regression: Online-SVR-FID. Mechanical Systems and Signal Processing, 2016, 76-77: 796-809 doi: 10.1016/j.ymssp.2016.02.056 [60] Liu J, Vitelli V, Zio E, Seraoui R. A novel dynamic-weighted probabilistic support vector regression-based ensemble for prognostics of time series data. IEEE Transactions on Reliability, 2015, 64 (4): 1203-1213 [61] Mao W T, He J L, Zuo M J. Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Transactions on Instrumentation and Measurement, 2020, 69(4): 1594-1608 doi: 10.1109/TIM.2019.2917735 [62] Soualhi A, Medjaher K, Zerhouni N. Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 2015, 64(1): 52-62 doi: 10.1109/TIM.2014.2330494 [63] Sun F Q, Li X Y, Liao H T, Zhang X K. A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component. Advances in Mechanical Engineering, 2017, 9(1): 1-9 [64] Nieto P J G, García-Gonzalo E, Lasheras F S, de Cos Juez F J. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System Safety, 2015, 138: 219-231 [65] Khelif R, Chebel-Morello B, Malinowski S, Laajili E, Fnaiech F, Zerhouni N. Direct remaining useful life estimation based on support vector regression. IEEE Transactions on Industrial Electronics, 2017, 64(3): 2276-2285 (本条文献与第60条文献重复, 请联系作者确认) doi: 10.1109/TIE.2016.2623260 [66] Huang H Z, Wang H K, Li Y F, Zhang L L, Liu Z L. Support vector machine based estimation of remaining useful life: Current research status and future trends. Journal of Mechanical Science and Technology, 2015, 29(1): 151-163 doi: 10.1007/s12206-014-1222-z [67] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). Budapest, Hungary: IEEE, 2004. 985−990 [68] Chaves I C, de Paula M R P, Leite L G M, Gomes J P P, Machado J C. Hard disk drive failure prediction method based on a Bayesian network. In: Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil: IEEE, 2018. 1−7 [69] Wu D Z, Jennings C, Terpenny J, Gao R X, Kumara S. A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 2017, 139(7): Article No. 071018 doi: 10.1115/1.4036350 [70] Singh S K, Kumar S, Dwivedi J P. A novel soft computing method for engine RUL prediction. Multimedia Tools and Applications, 2019, 78(4): 4065-4087 doi: 10.1007/s11042-017-5204-x [71] Wang T Y, Yu J B, Siegel D, Lee J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: Proceedings of the 2008 International Conference on Prognostics and Health Management. Denver, USA: IEEE, 2008. 1−6 [72] Ramasso E. Investigating computational geometry for failure prognostics. International Journal of Prognostics and Health Management, 2014, 5(1): Article No. 98 [73] Yu J, Tan M, Zhang H Y, Rui Y, Tao D C. Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(2): 563-578 doi: 10.1109/TPAMI.2019.2932058 [74] Wu S, Roberts K, Datta S, Du J C, Ji Z C, Si Y Q, et al. Deep learning in clinical natural language processing: A methodical review. Journal of the American Medical Informatics Association, 2020, 27(3): 457-470 doi: 10.1093/jamia/ocz200 [75] Nassif A B, Shahin I, Attili I, Azzeh M, Shaalan K. Speech recognition using deep neural networks: A systematic review. IEEE Access, 2019, 7: 19143-19165 doi: 10.1109/ACCESS.2019.2896880 [76] 雷亚国, 杨彬, 杜兆钧, 吕娜. 大数据下机械装备故障的深度迁移诊断方法. 机械工程学报, 2019, 55(7): 1-8 doi: 10.3901/JME.2019.07.001Lei Ya-Guo, Yang Bin, Du Zhao-Jun, Lü Na. Deep transfer diagnosis method for machinery in big data era. Journal of Mechanical Engineering, 2019, 55(7): 1-8 doi: 10.3901/JME.2019.07.001 [77] Al-Dulaimi A, Zabihi S, Asif A, Mohammadi A. A multimodal and hybrid deep neural network model for Remaining Useful Life estimation. Computers in Industry, 2019, 108: 186-196 doi: 10.1016/j.compind.2019.02.004 [78] 周福娜, 高育林, 王佳瑜, 文成林. 基于深度学习的缓变故障早期诊断及寿命预测. 山东大学学报(工学版), 2017, 47(5): 30-37Zhou Fu-Na, Gao Yu-Lin, Wang Jia-Yu, Wen Cheng-Lin. Early diagnosis and life prognosis for slowlyvarying fault based on deep learning. Journal of Shandong University (Engineering Science), 2017, 47(5): 30-37 [79] Xia M, Li T, Shu T X, Wan J F, de Silva C W, Wang Z R. A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3703-3711 doi: 10.1109/TII.2018.2868687 [80] Ren L, Cui J, Sun Y Q, Cheng X J. Multi-bearing remaining useful life collaborative prediction: A deep learning approach. Journal of Manufacturing Systems, 2017, 43: 248-256 doi: 10.1016/j.jmsy.2017.02.013 [81] Huang F M, Zhang J, Zhou C B, Wang Y H, Huang J S, Zhu L. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides, 2020, 17(1): 217-229 doi: 10.1007/s10346-019-01274-9 [82] Lee S, Kim H J, Kim S B. Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing. Applied Soft Computing, 2020, 86: Article No. 105904 doi: 10.1016/j.asoc.2019.105904 [83] Balevi E, Andrews J G. Autoencoder-based error correction coding for one-bit quantization. IEEE Transactions on Communications, 2020, 68(6): 3440-3451 doi: 10.1109/TCOMM.2020.2977280 [84] Ma J, Su H, Zhao W L, Liu B. Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self-learning. Complexity, 2018, 2018: Article No. 3813029 [85] 安华, 王国锋, 王喆, 马凯乐, 钟才川. 基于深度学习理论的刀具状态监测及剩余寿命预测方法. 电子测量与仪器学报, 2019, 33(9): 64-70An Hua, Wang Guo-Feng, Wang Zhe, Ma Kai-Le, Zhong Cai-Chuan. Deep learning-based method for tool condition monitoring and remaining useful life prediction. Journal of Electronic Measurement and Instrumentation, 2019, 33(9): 64-70 [86] Jiao R H, Peng K X, Dong J, Zhang K, Zhang C. A health indicator construction method based on deep belief network for remaining useful life prediction. In: Proceedings of the 2019 Prognostics and System Health Management Conference (PHM-Qingdao). Qingdao, China: IEEE, 2019. 1−6 [87] LeCun Y, Bengio Y. Convolutional networks for images, speech, and time-series [Online], available: https://www.researchgate.net/publication/2453996, June 1, 2021 [88] Ren L, Sun Y Q, Wang H, Zhang L. Prediction of bearing remaining useful life with deep convolution neural network. IEEE Access, 2018, 6: 13041-13049 doi: 10.1109/ACCESS.2018.2804930 [89] Wang B, Lei Y G, Li N P, Yan T. Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 2019, 134: Article No. 106330 doi: 10.1016/j.ymssp.2019.106330 [90] Zhu J, Chen N, Peng W W. Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3208-3216 doi: 10.1109/TIE.2018.2844856 [91] Liu R N, Yang B Y, Hauptmann A G. Simultaneous bearing fault recognition and remaining useful life prediction using joint-loss convolutional neural network. IEEE Transactions on Industrial Informatics, 2020, 16(1): 87-96 doi: 10.1109/TII.2019.2915536 [92] Babu G S, Zhao P L, Li X L. Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Proceedings of the 21st International Conference on Database Systems for Advanced Applications. Dallas, USA: Springer, 2016. 214−228 [93] Yang B Y, Liu R N, Zio E. Remaining useful life prediction based on a double-convolutional neural network architecture. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9521-9530 doi: 10.1109/TIE.2019.2924605 [94] Kwon S J, Han D, Choi J H, Lim J H, Lee S E, Kim J. Remaining-useful-life prediction via multiple linear regression and recurrent neural network reflecting degradation information of 20Ah LiNi_xMn_yCo_1?x?yO_2 pouch cell. Journal of Electroanalytical Chemistry, 2020, 858: Article No. 113729 doi: 10.1016/j.jelechem.2019.113729 [95] Li X Q, Jiang H K, Xiong X, Shao H D. Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network. Mechanism and Machine Theory, 2019, 133: 229-249 doi: 10.1016/j.mechmachtheory.2018.11.005 [96] Gugulothu N, Vishnu T V, Malhotra P, Vig L, Agarwal P, Shroff G. Predicting remaining useful life using time series embeddings based on recurrent neural networks [Online], available: https://arxiv.org/abs/1709.01073, June 1, 2021 [97] Miao H H, Li B, Sun C, Liu J. Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5023-5032 doi: 10.1109/TII.2019.2900295 [98] Wu Y T, Yuan M, Dong S P, Lin L, Liu Y Q. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 2018, 275: 167-179 doi: 10.1016/j.neucom.2017.05.063 [99] Elsheikh A, Yacout S, Ouali M S. Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing, 2019, 323: 148-156 doi: 10.1016/j.neucom.2018.09.076 [100] Zhang Y Z, Xiong R, He H W, Pecht M G. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5695-5705 doi: 10.1109/TVT.2018.2805189 [101] Huang C G, Huang H Z, Li Y F. A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8792-8802 doi: 10.1109/TIE.2019.2891463 [102] Yu Y, Hu C H, Si X S, Zheng J F, Zhang J X. Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset. Neurocomputing, 2020, 402: 134-147 doi: 10.1016/j.neucom.2020.03.041 [103] Zhang C, Lim P, Qin A K, Tan K C. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2306-2318 (本条文献与第86条文献重复, 请联系作者确认) doi: 10.1109/TNNLS.2016.2582798 [104] Deutsch J, He D. Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(1): 11-20 doi: 10.1109/TSMC.2017.2697842 [105] Ellefsen A L, Bj\orlykhaug E, \AEs\oy, V, Ushakov S, Zhang H X. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliability Engineering & System Safety, 2019, 183: 240-251 [106] Malhotra P, Vig T V, Ramakrishnan A, Anand G, Vig L, Agarwal P, et al. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. In: Proceedings of the 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management. San Francisco, USA, 2016. [107] Yu W N, Kim I Y, Mechefske C. Analysis of different RNN autoencoder variants for time series classification and machine prognostics. Mechanical Systems and Signal Processing, 2021, 149: Article No. 107322 doi: 10.1016/j.ymssp.2020.107322 [108] Pillai S, Vadakkepat P. Two stage deep learning for prognostics using multi-loss encoder and convolutional composite features. Expert Systems With Applications, 2021, 171: Article No. 114569 doi: 10.1016/j.eswa.2021.114569 [109] Ren L, Sun Y Q, Cui J, Zhang L. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 2018, 48: 71-77 doi: 10.1016/j.jmsy.2018.04.008 [110] Kapur K C, Pecht M. Reliability Engineering. New Jersey: John Wiley, 2014. [111] 文成林, 吕菲亚, 包哲静, 刘妹琴. 基于数据驱动的微小故障诊断方法综述. 自动化学报, 2016, 42(9): 1285-1299Wen Cheng-Lin, Lv Fei-Ya, Bao Zhe-Jing, Liu Mei-Qin. A review of data driven-based incipient fault diagnosis. Acta Automatica Sinica, 2016, 42(9): 1285-1299 [112] Ye Z S, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry, 2015, 31(1): 16-32 doi: 10.1002/asmb.2063 [113] 司小胜, 胡昌华, 周东华. 带测量误差的非线性退化过程建模与剩余寿命估计. 自动化学报, 2013, 39(5): 530-541Si Xiao-Sheng, Hu Chang-Hua, Zhou Dong-Hua. Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error. Acta Automatica Sinica, 2013, 39(5): 530-541 [114] 周东华, 魏慕恒, 司小胜. 工业过程异常检测、寿命预测与维修决策的研究进展. 自动化学报, 2013, 39(6): 711-722Zhou Dong-Hua, Wei Mu-Heng, Si Xiao-Sheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes. Acta Automatica Sinica, 2013, 39(6): 711-722 [115] 韩中, 程林, 熊金泉, 刘满君. 大数据结构化与数据驱动的复杂系统维修决策. 自动化学报, 2020, 46(2): 385-396Han Zhong, Cheng Lin, Xiong Jin-Quan, Liu Man-Jun. Complex system maintenance decisions based on big data structuration and data-driven. Acta Automatica Sinica, 2020, 46(2): 385-396 [116] Sato K I. Lévy Processes and Infinitely Divisible Distributions. Cambridge: Cambridge University Press, 1999. [117] 裴洪, 胡昌华, 司小胜, 张正新, 杜党波. 不完美维护下基于剩余寿命预测信息的设备维护决策模型. 自动化学报, 2018, 44(4): 719-729Pei Hong, Hu Chang-Hua, Si Xiao-Sheng, Zhang Zheng-Xin, Du Dang-Bo. Remaining life prediction information-based maintenance decision model for equipment under imperfect maintenance. Acta Automatica Sinica, 2018, 44(4): 719-729 [118] 任子强, 司小胜, 胡昌华, 王玺. 融合多传感器数据的发动机剩余寿命预测方法. 航空学报, 2019, 40(12): 129-140Ren Zi-Qiang, Si Xiao-Sheng, Hu Chang-Hua, Wang Xi. Remaining useful life prediction method for engine combining multi-sensors data. Acta Aeronautica et Astronautica Sinica, 2019, 40(12): 129-140 [119] Li N P, Gebraeel N, Lei Y G, Bian L K, Si X S. Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model. Reliability Engineering & System Safety, 2019, 186: 88-100 [120] Zhang Z X, Si X S, Hu C H, Lei Y G. Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research, 2018, 271(3): 775-796 doi: 10.1016/j.ejor.2018.02.033 [121] Gebraeel N Z, Lawley M A, Li R, Ryan J K. Residual-life distributions from component degradation signals: A Bayesian approach. IIE Transactions, 2005, 37(6): 543-557 doi: 10.1080/07408170590929018 [122] Huang Z Y, Xu Z G, Wang W H, Sun Y X. Remaining useful life prediction for a nonlinear heterogeneous wiener process model with an adaptive drift. IEEE Transactions on Reliability, 2015, 64(2): 687-700 doi: 10.1109/TR.2015.2403433 [123] Si X S, Wang W B, Hu C H, Zhou D H, Pecht M G. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability, 2012, 61(1): 50-67 doi: 10.1109/TR.2011.2182221 [124] Zhang J X, Hu C H, He X, Si X S, Liu Y, Zhou D H. A novel lifetime estimation method for two-phase degrading systems. IEEE Transactions on Reliability, 2019, 68(2): 689-709 doi: 10.1109/TR.2018.2829844 [125] Li T M, Pei H, Pang Z N, Si X S, Zheng J F. A sequential Bayesian updated Wiener process model for remaining useful life prediction. IEEE Access, 2019, 8: 5471-5480 [126] Si X S, Zhang Z X, Hu C H. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Berlin, Germany: Springer-Verlag, 2017. [127] Peng W W, Li Y F, Mi J H, Yu L, Huang H Z. Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective. Reliability Engineering & System Safety, 2016, 153: 75-87 [128] Nelsen R B. An Introduction to Copulas (2nd edition). New York, USA: Springer, 2006. [129] Pan Z Q, Balakrishnan N, Sun Q, Zhou J L. Bivariate degradation analysis of products based on Wiener processes and copulas. Journal of Statistical Computation and Simulation, 2013, 83(7): 1316-1329 doi: 10.1080/00949655.2012.658805 [130] Peng W W, Li Y F, Yang Y J, Zhu S P, Huang H Z. Bivariate analysis of incomplete degradation observations based on inverse Gaussian processes and copulas. IEEE Transactions on Reliability, 2016, 65(2): 624-639 doi: 10.1109/TR.2015.2513038 [131] 刘胜南, 陆宁云, 程月华, 姜斌, 邢琰. 基于多退化量的动量轮剩余寿命预测方法. 南京航空航天大学学报, 2015, 47(3): 360-366Liu Sheng-Nan, Lu Ning-Yun, Cheng Yue-Hua, Jiang Bin, Xing Yan. Remaining lifetime prediction for momentum wheel based on multiple degradation parameters. Journal of Nanjing University of Aeronautics & Astronautics, 2015, 47(3): 360-366 [132] 张建勋, 胡昌华, 周志杰, 司小胜, 杜党波. 多退化变量下基于Copula函数的陀螺仪剩余寿命预测方法. 航空学报, 2014, 35(4): 1111-1121Zhang Jian-Xun, Hu Chang-Hua, Zhou Zhi-Jie, Si Xiao-Sheng, Du Dang-Bo. Multiple degradation variables modeling for remaining useful life estimation of gyros based on copula function. Acta Aeronautica et Astronautica Sinica, 2014, 35(4): 1111-1121 [133] Liu K B, Huang S. Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering, 2016, 13(1): 344-354 doi: 10.1109/TASE.2014.2349733 [134] Kim M, Song C Y, Liu K B. A generic health index approach for multisensor degradation modeling and sensor selection. IEEE Transactions on Automation Science and Engineering, 2019, 16(3): 1426-1437 doi: 10.1109/TASE.2018.2890608 [135] Liu K B, Chehade A, Song C Y. Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 2017, 14(3): 1504-1514 doi: 10.1109/TASE.2015.2446752 [136] Yan H, Liu K B, Zhang X, Shi J J. Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions. IEEE Transactions on Reliability, 2016, 65(3): 1416-1426 doi: 10.1109/TR.2016.2575449 [137] Liu K B, Gebraeel N Z, Shi J J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 652-664 doi: 10.1109/TASE.2013.2250282 [138] Deutsch J, He M, He D. Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach. Applied Sciences, 2017, 7(7): Article No. 649 doi: 10.3390/app7070649 [139] 彭开香, 皮彦婷, 焦瑞华, 唐鹏. 航空发动机的健康指标构建与剩余寿命预测. 控制理论与应用, 2020, 37(4): 713-720Peng Kai-Xiang, Pi Yan-Ting, Jiao Rui-Hua, Tang Peng. Health indicator construction and remaining useful life prediction for aircraft engine. Control Theory & Applications, 2020, 37(4): 713-720 [140] Peng K X, Jiao R H, Dong J, Pi Y T. A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 2019, 361: 19-28 doi: 10.1016/j.neucom.2019.07.075 [141] Hu C H, Pei H, Si X S, Du D B, Pang Z N, Wang X. A prognostic model based on DBN and diffusion process for degrading bearing. IEEE Transactions on Industrial Electronics, 2020, 67(10): 8767-8777 doi: 10.1109/TIE.2019.2947839 [142] Saxena A, Goebel K. C-MAPSS data set, NASA Ames Prognostics Data Repository, 2008. [143] Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. In: Proceedings of the 2008 International Conference on Prognostics and Health Management. Denver, USA: IEEE, 2008. [144] 任子强. 融合多传感器数据的随机退化设备健康管理方法研究 [硕士学位论文], 火箭军工程大学, 中国, 2019.Ren Zi-Qiang. Research on Health Management Method for Stochastic Degrading Equipment Via Integrating Multi-sensors Data [Master thesis], Rocket Force University of Engineering, China, 2019. [145] Saha B, Goebel K. Battery data set: NASA Ames Prognostics Data Repository, NASA Ames, Moffett Field, CA, 2007 [Online], available: http://ti.arc.nasa.gov/project/prognos [146] Battery Research Data. Center for advanced life cycle engineering (CALCE): University of Maryland [Online], available: http://calce.umd.edu/data [147] Peng W W, Ye Z S, Chen N. Bayesian deep-learning-based health prognostics toward prognostics uncertainty. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2283-2293 doi: 10.1109/TIE.2019.2907440 [148] Wang B, Lei Y G, Yan T, Li N P, Guo L. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2020, 379: 117-129 doi: 10.1016/j.neucom.2019.10.064 -
图(3) / 表(3)
计量
- 文章访问数: 3646
- HTML全文浏览量: 1673
- PDF下载量: 1012
- 被引次数: 0
