朱之丽 , 陈敏 , 唐海龙 , 等 . 航空燃气涡轮发动机工作原理及性能 [M ] . 上海 : 上海交通大学出版社 , 2018 : 257 - 261 .

ZHU Z L , CHEN M , TANG H L , et al . Working principle and performance of aircraft gas turbine engines [M ] . Shanghai : Shanghai Jiao Tong University Press , 2018 : 257 - 261 . (in Chinese)

周忠宝 , 马超群 , 周经伦 . 基于动态贝叶斯网络的动态故障树分析 [J ] . 系统工程理论与实践 , 2008 , 28 ( 2 ): 35 - 42 . DOI: 10.12011/1000-6788(2008)2-35 http://doi.org/10.12011/1000-6788(2008)2-35

ZHOU Z B , MA C Q , ZHOU J L . Dynamic fault tree analysis based on dynamic bayesiannetworks [J ] . System Engineering-Theory & Practice , 2008 , 8 ( 2 ): 35 - 42 . (in Chinese)

蔡鼎 . 融合先验知识的燃气轮机欠定气路故障诊断方法研究 [D ] . 哈尔滨 : 哈尔滨工业大学 , 2105 : 40 - 42 .

CAI D . Method forunderdetermined fault diagnosis with the prior knowledge of gas turbine [D ] . Harbin : Harbin Institute of Technology , 2015 : 40 - 42 . (in Chinese)

金山 . 复杂系统可靠性建模与分析 [M ] . 北京 : 国防工业出版社 , 2015 : 5 - 17 .

JIN S . Complexsystem reliability modeling and analysis [M ] . Beijing : National Defense Industry Press , 2015 : 5 - 17 . (in Chinese)

杨洁 , 万平安 , 王景霖 , 等 . 基于多传感器融合卷积神经网络的航空发动机轴承故障诊断 [J ] . 中国电机工程学报 , 2022 , 42 ( 13 ): 4933 - 4941 .

YANG J , WAN P A , WANG J L , et al . Aeroengine bearing fault diagnosis based on convolutional neural network for multi-sensor information fusion [J ] . Proceedings of the CSEE , 2022 , 42 ( 13 ): 4933 - 4941 . (in Chinese)

魏晓良 , 潮群 , 陶建峰 , 等 . 基于LSTM和CNN的高速柱塞泵故障诊断 [J ] . 航空学报 , 2021 , 42 ( 3 ): 435 - 445 .

WEI X L , CHAO Q , TAO J F , et al . Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN [J ] . Acta Aeronautica et Astronautica Sinica , 2021 , 42 ( 3 ): 435 - 445 . (in Chinese)

WOO S , PARK J , LEE J Y , et al . CBAM: convolutional block attention module [C ] // Proceedings of the 15th European Conference on Computer Vision.Munich , Germany : Springer , 2018 : 3 - 19 .

ZHAO M H , ZHONG S S , FU X Y , et al . Deep residual shrinkage networks for fault diagnosis [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 7 ): 4681 - 4690 . DOI: 10.1109/TII.9424 http://doi.org/10.1109/TII.9424 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424

DING Y , MA L , MA J , et al . Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification [J ] . Advanced Engineering Informatics , 2019 , 42 : 100977 . DOI: 10.1016/j.aei.2019.100977 http://doi.org/10.1016/j.aei.2019.100977 https://linkinghub.elsevier.com/retrieve/pii/S1474034619305506 https://linkinghub.elsevier.com/retrieve/pii/S1474034619305506

邓飞跃 , 丁浩 , 吕浩洋 , 等 . 一种基于轻量级神经网络的高铁轮对轴承故障诊断方法 [J ] . 工程科学学报 , 2021 , 43 ( 11 ): 1482 - 1490 .

DENG F Y , DING H , LÜ H Y , et al . Fault diagnosis of high-speed train wheelset bearing based on a lightweight neural network [J ] . Chinese Journal of Engineering , 2021 , 43 ( 11 ): 1482 - 1490 . (in Chinese)

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)

SHOKROLAHI S M , KARIMIZIARANI M . A deep network solution for intelligent fault detection in analog circuit [J ] . Analog Integrated Circuits and Signal Processing , 2021 , 107 ( 3 ): 597 - 604 . DOI: 10.1007/s10470-020-01732-8 http://doi.org/10.1007/s10470-020-01732-8

HOWARD A G , ZHU M L , CHEN B , et al . Mobilenets: Efficientconvolutional neural networks for mobile vision Applications:arXiv:1704.04861 [R/OL ] . Ithaca, NY , US : Cornell University .(2017-04-17) [2020-12-09 ] . https://arxiv.org/abs/1704.04861 https://arxiv.org/abs/1704.04861 https://arxiv.org/abs/1704.04861.

BAI S J , ZICO K , VLADLEN K . An empirical evaluation of generic convolutional and recurrent networks for sequence modeling:arXiv:1803.01271 [R/OL ] . Ithaca, NY , US : Cornell University .(2018-03-04). https://arxiv.org/abs/1803.01271 https://arxiv.org/abs/1803.01271 https://arxiv.org/abs/1803.01271.

皮骏 , 黄江博 . 基于IPSO-Elman神经网络的航空发动机故障诊断 [J ] . 航空动力学报 , 2017 , 32 ( 12 ): 3031 - 3038 .

PI J , HUANG J B . Aero-engine fault diagnosis based on IPSO-Elman neural network [J ] . Journal of Aerospace Power , 2017 , 32 ( 12 ): 3031 - 3038 . (in Chinese)

谭海旺 , 杨启亮 , 邢建春 , 等 . 基于XGBoost-LSTM组合模型的光伏发电功率预测 [J ] . 太阳能学报 , 2022 , 43 ( 8 ): 75 - 81 . DOI: 10.19912/j.0254-0096.tynxb.2021-0005 http://doi.org/10.19912/j.0254-0096.tynxb.2021-0005 该文提出一种基于极端梯度提升（XGBoost）模型和长短期记忆网络（LSTM）模型的短期光伏发电功率预测组合模型。根据短期光伏发电特性,首先分别建立XGBoost模型和LSTM模型,然后利用XGBoost模型进行初步预测增加特征,并利用误差倒数法将两模型组合起来进行预测。选取2018年光伏电站人工智能运维大数据处理分析大赛的数据集进行实验评估,最终结果表明,该文所构建的XGBoost-LSTM组合模型的均方根误差（RMSE）为0.214,将上述方法与随机森林、GBDT模型和单一的XGBoost模型和LSTM模型相比较,该文提出的方法具有更高的预测精度。

TAN H W , YANG Q L , XING J C , et al . Photovoltaic output prediction based on combined XGBoost-LSTM model [J ] . Acta Energiae Solaris Sinica , 2022 , 43 ( 8 ): 75 - 81 . (in Chinese)

马怀祥 , 冯旭威 , 李东升 , 等 . 基于CNN和XGBoost的滚动轴承故障诊断方法 [J ] . 中国工程机械学报 , 2019 , 19 ( 3 ): 254 - 259 .

MA H X , FENG X W , LI D S , et al . Fault diagnosis method of rolling bearing based on CNN and XGBoost [J ] . Chinese Journal of Construction Machinery , 2019 , 19 ( 3 ): 254 - 259 . (in Chinese)

AARON V D O , SANDER D , HEIGA Z . Wavenet:a generative model for raw audio:arXiv :1609.03499 [R/OL ] . Ithaca,NY , US : Cornell University , 2016 :1609.03499.(2016-09-19). https://arxiv.org/abs/1609.03499 https://arxiv.org/abs/1609.03499 https://arxiv.org/abs/1609.03499.

HE Y D , ZHAO J B . Temporal convolutional networks for anomaly detection in time series [J ] . Journal of Physics: Conference Series , 2013 , 1213 ( 4 ): 042050 . DOI: 10.1088/1742-6596/1213/4/042050 http://doi.org/10.1088/1742-6596/1213/4/042050 Convolutional Networks have been demonstrated to be particularly useful for extracting high level feature in structural data. Temporal convolutional network (TCN) is a framework which employs casual convolutions and dilations so that it is adaptive for sequential data with its temporality and large receptive fields. In this paper, we apply TCN for anomaly detection in time series. We train the TCN on normal sequences and use it to predict trend in a number of time steps. Prediction errors are fitted by a multivariate Gaussian distribution and used to calculate the anomaly scores of points. In addition, a multi-scale feature mixture method is raised to promote performance. The validity of this method is confirmed on three real-world datasets.

HU J , SHEN L , SUN G . Squeeze-and-eitation networks [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, US:IEEE , 2019 : 7132 - 7141 .

华校专 . AI算法工程师手册 [R/OL ] .(2022-04-03)[2022-04-08 ] . http://www.huaxiaozhuan.com/ http://www.huaxiaozhuan.com/ http://www.huaxiaozhuan.com/.

HUA X Z . AI algorithms engineer handbook [R/OL ] . (2022-04-03)[2022-04-08 ] . http://www.huaxiaozhuan.com/ http://www.huaxiaozhuan.com/ http://www.huaxiaozhuan.com/. (in Chinese)

杜先君 , 巩彬 , 余萍 , 等 . 基于CBAM-CNN的模拟电路故障诊断 [J ] . 控制与决策 , 2022 , 37 ( 10 ): 2609 - 2618 .

DU X J , GONG B , YU P , et al . Research on CBAM-CNN based analog circuit fault diagnosis [J ] . Control and Decision , 2022 , 37 ( 10 ): 2609 - 2618 . (in Chinese)

田彦云 . 涡轴发动机建模与扭矩加载控制技术研究 [D ] . 南京 : 南京航空航天大学 , 2016 .

TIAN Y Y . Research on modeling and torque loading control technology of turbo-shaft [D ] . Nanjing : Nanjing University of Aeronautics and Astronautics , 2016 . (in Chinese)

张浩 , 李密 , 赵海刚 . 基于试飞数据的涡轴发动机性能建模技术 [J ] . 推进技术 , 2021 , 42 ( 10 ): 2177 - 2186 .

ZHANG H , LI M , ZHAO H G . Performance modeling technique of turboshaft engine based on flight experimental data [J ] . Journal of Propulsion Technology , 2021 , 42 ( 10 ): 2177 - 2186 . (in Chinese)

潘慕绚 , 陈强龙 , 周永权 , 等 . 涡扇发动机多动力学建模方法 [J ] . 航空学报 , 2019 , 40 ( 5 ): 99 - 110 .

PAN M X , CHEN Q L , ZHOU Y Q , et al . Amulti-dynamicsapproachto turbofan engine modeling [J ] . Acta Aeronautica et Astronautica Sinica , 2019 , 40 ( 5 ): 99 - 110 . (in Chinese)

LU F , JU H F , HUANG J Q . An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring [J ] . Aerospace Science and Technology , 2016 , 58 ( 1 ): 36 - 47 . DOI: 10.1016/j.ast.2016.08.008 http://doi.org/10.1016/j.ast.2016.08.008 https://linkinghub.elsevier.com/retrieve/pii/S1270963816304187 https://linkinghub.elsevier.com/retrieve/pii/S1270963816304187

楚娜娜 , 张曙光 , 高艳蕾 , 等 . 基于Simscape模型的航空发动机系统安全性分析方法 [J ] . 航空动力学报 , 2021 , 36 ( 4 ): 885 - 896 .

CHU N N , ZHANG S G , GAO Y L , et al . Safety analysis method of aero-engine systems based on simscape model [J ] . Journal of Aerospace Power , 2021 , 36 ( 4 ): 885 - 896 . (in Chinese)

刘星怡 . 基于深度信念网络的航空发动机气路故障诊断方法研究 [D ] . 南京 : 南京航空航天大学 , 2020 .

LIU X Y . Research ongas path fault diagnosis method for aeroengine based on deep belief network [D ] . Nanjing : Nanjing University of Aeronautics and Astronautics , 2020 . (in Chinese)

葛怡 . 航空发动机几种典型故障建模仿真及预测 [D ] . 西安 : 西安理工大学 , 2020 .

GE Y . Modeling, simulation and prediction of several typical faults of aeroengine [D ] . Xi’an : Xi’an University of Technology , 2020 . (in Chinese)

WEI T T , VAN BEEK A , HAO J R , et al . Bayesian calibration of performance degradation in a gas turbine-driven compressor unit for prognosis health management [J ] . Journal of Engineering for Gas Turbines and Power , 2022 , 144 ( 5 ): 51014 . DOI: 10.1115/1.4053564 http://doi.org/10.1115/1.4053564 https://asmedigitalcollection.asme.org/gasturbinespower/article/144/5/051014/1131408/Bayesian-Calibration-of-Performance-Degradation-in https://asmedigitalcollection.asme.org/gasturbinespower/article/144/5/051014/1131408/Bayesian-Calibration-of-Performance-Degradation-in Prognosis health management is an effective way to improve the operational safety and economy of industrial equipment. The development of an accurate and quick response model to monitor equipment health status, predict performance, and diagnose faults is key to its implementation. However, the inevitable performance degradation of industrial equipment over time poses a significant challenge to such a model. In this work, we adopt a Bayesian approach to calibrate thermodynamic simulations with time-dependent parameters that account for performance degradation. The relationship between degradation and time is modeled through an assumed functional form, referred to as a health indicator. The proposed health indicator calibration method gives a rapid assessment of degraded equipment performance and elucidates how degradation relates to time. The novelty of this paper is that it regards performance degradation as an uncertainty quantification problem rather than a deterministic problem. The health indicator calibration method is validated on a natural gas compressor and a gas turbine. The results show that when severe degradation occurs, functional calibration improves predictive performance over nonfunctional calibration (i.e., independent of time). The introduced method provides valuable decision support to extend the service life and reduce maintenance costs for industrial equipment. Its feedback in operation can also develop the service assessment criteria and inform the design of subsequent generations of industrial equipment.

ZHAO Y P , CHEN Y B . Extreme learning machine based transfer learning for aero engine fault diagnosis [J ] . Aerospace Scienece and Technology , 2022 , 121 : 107311 .

石重托 , 姚伟 , 黄彦浩 , 等 . 基于SE-CNN和仿真数据电力系统主导失稳模式智能识别 [J/OL ] . 中国电机工程学报 .( 2021-11-08 ). https://kns.cnki.net/kcms/detail/11.2107.TM.2021110-5.1639.046.html https://kns.cnki.net/kcms/detail/11.2107.TM.2021110-5.1639.046.html https://kns.cnki.net/kcms/detail/11.2107.TM.2021110-5.1639.046.html.

SHI Z T , YAO W , HUANG Y H , et al . Power system dominant instability model identification based on convolutional neural networks with squeeze and excitation block and simulation data [J ] . Proceedings of the CSEE . (2021-11-08). https://kns.cnki.net/kcms/detail/11.2107.TM.20211105.1639.046.html https://kns.cnki.net/kcms/detail/11.2107.TM.20211105.1639.046.html https://kns.cnki.net/kcms/detail/11.2107.TM.20211105.1639.046.html. (in Chinese)

张浩 , 胡昌华 , 杜党波 , 等 . 多状态影响下基于Bi-LSTM网络的锂电池剩余寿命预测方法 [J ] . 电子学报 , 2022 , 50 ( 3 ): 619 - 624 . DOI: 10.12263/DZXB.20210207 http://doi.org/10.12263/DZXB.20210207 现有基于深度学习的锂电池剩余寿命（Remaining Useful Life, RUL）预测方法中，锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此，提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先，引入双向长短时记忆（Bi?directional Long Short?Term Memory, Bi?LSTM）网络学习三种状态数据的时间相关性.其次，利用dropout技术与Bayesian变分推断技术间的等价性实现了RUL预测结果的不确定性量化，得到了预测结果的95%置信区间与概率密度分布（Probability Density Function, PDF），并分析了不同dropout率对预测不确定性的影响.最后，通过四种不同的深度学习模型框架与两种内部状态输入方案的对比实验，验证了本文方法的有效性.

ZHANG H , HU C H , DU D B , et al . Remaining useful life prediction method of lithium-Ion battery based on Bi-LSTM network under multi-state influence [J ] . Acta Electronica Sinica , 2022 , 50 ( 3 ): 619 - 624 . (in Chinese) DOI: 10.12263/DZXB.20210207 http://doi.org/10.12263/DZXB.20210207 The life information contained in multiple internal states of lithium-ion battery is not fully considered in the existing RUL(Remaining Useful Life) prediction methods of lithium-ion battery based on deep learning. In view of this, a RUL prediction model that integrates the three internal states include battery capacity, impedance and temperature is proposed. The Bi-LSTM(Bi-directional Long Short-Term Memory) network is introduced to learn the correlation about time of the data of the three states firstly. Secondly, the equivalence between dropout technology and Bayesian variational inference technology is used to quantify the uncertainty of the RUL prediction results. The 95% confidence interval and PDF(Probability Distribution Function) of the RUL prediction results are obtained, and the effect on the prediction uncertainty of different dropout rates is analyzed. Finally, the effectiveness of this method is verified through the comparative experiments of four different deep learning model and two input schemes of internal state.