机电装备健康状态评估研究进展及发展趋势
1 曾声奎, Pecht M G, 吴际. 故障预测与健康管理(PHM)技术的现状与发展[J]. 航空学报, 2005, 26(5): 610-632. Zeng Sheng-kui, Pecht M G, Wu Ji. Status and development of failure prediction and health management (PHM) technology[J]. Acta Aeronautica Sinica, 2005, 26(5): 610-632.2 国家制造强国建设战略咨询委员会. 中国制造2025蓝皮书(2017)[M]. 北京: 电子工业出版社, 2017.3 徐庆宏, 任和, 马小骏. 民用飞机实时监控与健康管理技术[M]. 上海: 上海交通大学出版社, 2018.4 刘宗长. 从人工智能到工业智能[J]. 软件和集成电路, 2018(6): 34-39. Liu Zong-chang. From artificial intelligence to industrial intelligence[J]. Software and Integrated Circuits, 2018(6): 34-39.5 李杰.云上工业智能[M].北京:中信出版集团,2017.6 董兴辉, 张鑫淼, 郑凯, 等. 基于组合赋权和云模型的风电机组健康状态评估[J]. 太阳能学报, 2018, 2(4): 68-75. Dong Xing-hui, Zhang Xin-miao, Zheng Kai, et al. Wind turbine health status assessment based on combined weighting and cloud model[J]. Acta Energia Sinica, 2018, 2(4): 68-75.7 Zheng K, Han L N, Guo S L, et al. Fuzzy synthetic condition assessment of wind turbine based on combination weighting and cloud model[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32(6): 4563-4572.8 Liu T I, Song S D, Liu G, et al. Online monitoring and measurements of tool wear for precision turning of stainless steel parts[J]. International Journal of Advanced Manufacturing Technology, 2017, 65(9-12): 1397-1407.9 胡姚刚, 李辉, 刘海涛, 等. 基于多类证据体方法的风电机组健康状态评估[J]. 太阳能学报, 2018, 39(2): 256-265. Hu Yao-gang, Li Hui, Liu Hai-tao, et al. Wind turbine health status assessment based on multi-class evidence body method[J]. Acta Energia Sinica, 2018, 39(2): 256-265.10 Sun Z X, Sun H X. Health status assessment for wind turbine with recurrent neural networks[J]. Mathematical Problems in Engineering. DOI: 10.1155/2018/6972481.
doi: 10.1155/2018/697248111 董玉亮, 顾煜炯. 基于保局投影与自组织映射的风电机组故障预警方法[J]. 太阳能学报, 2015, 36(5): 1123-1129. Dong Yu-liang, Gu Yu-jiong. Wind turbine fault warning method based on security bureau projection and self-organizing mapping[J]. Acta Energia Sinica, 2015, 36(5): 1123-1129.12 钟诗胜, 雷达. 一种可用于航空发动机健康状态预测的动态集成极端学习机模型[J]. 航空动力学报, 2016, 29(9): 2085-2090. Zhong Shi-sheng, Lei Da. A dynamic integrated extreme learning machine model that can be used to predict the health status of aero engines[J]. Journal of Aeronautical Dynamics, 2016, 29(9): 2085-2090.13 Tamilselvan P, Wang P F. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2020, 115: 124-135.14 Ma M, Sun C, Chen X F. Discriminative deep belief networks with ant colony optimization for health status assessment of machine[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(12): 3115-3125.15 Hanachi H, Liu J, Banerjee A, et al. A physics-based modeling approach for performance monitoring in gas turbine engines[J]. IEEE Transactions on Reliability, 2015, 64(1): 197-205.16 陈煜, 鞠红飞, 鲁峰, 等. 涡喷发动机健康状态的带约束非线性滤波估计[J]. 推进技术, 2016, 37(4): 741-748. Chen Yu, Ju Hong-fei, Lu Feng, et al. Constrained nonlinear filtering estimation of turbojet engine health state[J]. Propulsion Technology, 2016, 37(4): 741-748.17 Yin X J, Wang Z L, Zhang B C, et al. Health estimation of fan based on belief-rule-base expert system in turbofan engine gas-path[J]. Advances in Mechanical Engineering, 2017, 9(3): 1-11.18 Di Maio F, Hu J, Tse P, et al. Ensemble-approaches for clustering health status of oil sand pumps[J]. Expert Systems with Applications, 2020, 39(5): 4847-4859.19 王浩任, 黄亦翔, 赵帅, 等. 基于小波包和拉普拉斯特征值映射的柱塞泵健康评估方法[J]. 振动与冲击, 2017, 36(22): 45-50. Wang Hao-ren, Huang Yi-xiang, Zhao Shuai, et al. Health assessment method of plunger pump based on wavelet packet and laplace eigenvalue mapping[J]. Journal of Vibration and Shock, 2017, 36(22): 45-50.20 Diez-Olivan A, Pagan J A, Khoa N L D, et al. Kernel-based support vector machines for automated health status assessment in monitoring sensor data[J]. International Journal of Advanced Manufacturing Technology, 2018, 95(1-4): 327-340.21 Arshad M, Islam S M, Khaliq A. Fuzzy logic approach in power transformers management and decision making[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2018, 21(5): 2343-2354.22 王亮, 吕卫民, 金永川. 一种多类型证据的合成评估方法[J]. 控制与决策, 2017, 12(11): 1973-1978. Wang Liang, Lv Wei-min, Jin Yong-chuan. A composite evaluation method for multiple types of evidence[J]. Control and Decision, 2017, 12(11): 1973-1978.23 Cheng J, Yu M. OTHR health status assessment using grey clustering method[C]∥3rd International Conference on Mechanical, Control and Computer Engineering, Huhhot, 2018: 494-497.24 崔建国, 林泽力, 吕瑞, 等. 基于模糊灰色聚类和组合赋权法的飞机健康状态综合评估方法[J]. 航空学报, 2014, 35(3): 764-772. Cui Jian-guo, Lin Ze-li, Lv Rui, et al. Comprehensive assessment method of aircraft health status based on fuzzy grey clustering and combined weighting method[J]. Acta Aeronautica Sinica, 2014, 35(3): 764-772.25 Wen J, Gao H L. Degradation assessment for the ball screw with variational autoencoder and kernel density estimation[J]. Advances in Mechanical Engineering, 2018, 10(9): 1-12.26 邓超, 孙耀宗, 李嵘, 等. 基于隐Markov模型的重型数控机床健康状态评估[J]. 计算机集成制造系统, 2013, 19(3): 552-558. Deng Chao, Sun Yao-zong, Li Rong, et al. Health status assessment of heavy CNC machine tools based on hidden markov model[J]. Computer Integrated Manufacturing System, 2013, 19(3): 552-558.27 谷梦瑶, 陈友玲, 王新龙. 多退化变量下基于实时健康度的相似性寿命预测方法[J]. 计算机集成制造系统, 2017, 23(2): 362-372. Gu Meng-yao, Chen You-ling, Wang Xin-long. Similarity life prediction method based on real-time health under multiple degradation variables[J]. Computer Integrated Manufacturing System, 2017, 23(2): 362-372.28 Yang J Y, Zhang Y Y, Zhu Y S. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension[J]. Mechanical Systems and Signal Processing, 2017, 21(5): 2012-2024.29 Jiang H M, Chen J, Dong G M. Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment[J]. Mechanical Systems and Signal Processing, 2016, 72: 184-205.30 Yin X J, Zhang B C, Zhou Z J, et al. A new health estimation model for CNC machine tool based on infinite irrelevance and belief rule base[J]. Microelectronics Reliability, 2018, 84: 187-196.31 李巍华, 李静, 张绍辉. 连续隐半马尔科夫模型在轴承性能退化评估中的应用[J]. 振动工程学报, 2016, 27(4): 613-620. Li Wei-hua, Li Jing, Zhang Shao-hui. Application of continuous hidden semi-Markov model in bearing performance degradation assessment[J]. Journal of Vibration Engineering, 2016, 27(4): 613-620.32 Liao Z R, Gao D, Lu Y, et al. Multi-scale hybrid HMM for tool wear condition monitoring[J]. International Journal of Advanced Manufacturing Technology, 2016, 84: 2437-2448.33 Lu C, Li T Y, Liu H M. Online milling tool condition monitoring with a single continuous hidden Markov models approach[J]. Journal of Vibroengineering, 2017, 16(5): 2448-2457.34 Yu J S, Liang S, Tang D Y, et al. A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction[J]. International Journal of Advanced Manufacturing Technology, 2017, 91: 201-211.35 Kong D D, Chen Y J, Li N. Gaussian process regression for tool wear prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 556-574.36 Kong D D, Chen Y J, Li N. Hidden semi-markov model-based method for tool wear estimation in milling process[J]. International Journal of Advanced Manufacturing Technology, 2017, 92: 3647-3657.37 Kong D D, Chen Y J, Li N. Force-based tool wear estimation for milling process using gaussian mixture hidden markov models[J]. International Journal of Advanced Manufacturing Technology, 2017, 92: 2853-2865.38 刘美芳, 余建波, 尹纪庭. 基于贝叶斯推论和自组织映射的轴承性能退化评估方法[J]. 计算机集成制造系统, 2018, 18(10): 269-278. Liu Mei-fang, Yu Jian-bo, Yin Ji-ting. Bearing performance degradation assessment method based on bayesian inference and self-organizing map[J]. Computer Integrated Manufacturing System, 2018, 18(10): 269-278.39 Guo L, Li N P, Jia F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240: 98-109.40 Khoualdia T, Hadjadj A E, Bouacha K, et al. Multi-objective optimization of ANN fault diagnosis model for rotating machinery using grey rational analysis in taguchi method[J]. International Journal of Advanced Manufacturing Technology, 2017, 89: 3009-3020.41 Jiang H M, Chen J, Dong G M, et al. Study on hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2015, 52/53: 338-359.42 Lee G Y, Kim M, Quan Y J, et al. Machine health management in smart factory: a review[J]. Journal of Mechanical Science and Technology, 2018, 32(3): 987-1009.43 Luo S R, Cheng J S, Ao H. Application of LCD-SVD technique and CRO-SVM method to fault diagnosis for roller bearing[J]. Shock and Vibration, 2015, 2015: 847802.44 Yuan N Q, Yang W L, Kang B, et al. Signal fusion-based deep fast random forest method for machine health assessment[J]. Journal of Manufacturing Systems, 2018, 48: 1-8.45 李洪雪, 李世武, 孙文财, 等. 重型危险品半挂列车行驶工况的构建[J]. 吉林大学学报: 工学版, 2021, 51(5): 1700-1707. Li Hong-xue, Li Shi-wu, Sun Wen-cai, et al. Construction of driving conditions of heavy-duty dangerous goods semitrailer trains[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1700-1707.46 Benkedjouh T, Medjaher K, Zerhouni N, et al. Health assessment and life prediction of cutting tools based on support vector regression[J]. Journal of Intelligent Manufacturing, 2015, 26(2): 213-223.47 徐宇亮, 孙际哲, 陈西宏, 等. 电子设备健康状态评估与故障预测方法[J]. 系统工程与电子技术, 2015, 34(5): 1068-1072. Xu Yu-liang, Sun Ji-zhe, Chen Xi-hong, et al. Methods of electronic equipment health status assessment and failure prediction[J]. Systems Engineering and Electronic Technology, 2015, 34(5): 1068-1072.48 Ma M, Chen X F, Zhang X L, et al. Locally linear embedding on grassmann manifold for performance degradation assessment of bearings[J]. IEEE Transactions on Reliability, 2017, 66(2): 467-477.49 Li F, Chyu M K K, Wang J X, et al. Life grade recognition of rotating machinery based on Supervised orthogonal linear local tangent space alignment and optimal supervised fuzzy c-means clustering[J]. Measurement, 2015, 73: 384-400.50 Liao L X, Jin W J, Pavel R. Enhanced restricted boltzmann machine with prognosability regularization for prognostics and health assessment[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7076-7083.51 Medjaher K, Tobon-Mejia D A, Zerhouni N. Remaining useful life estimation of critical components with application to bearings[J]. IEEE Transactions on Reliability, 2019, 61(2): 292-302.52 Wang T. Bearing life prediction based on vibration signals: a case study and lessons learned[C]∥IEEE Conference on Prognostics and Health Management, Denver, 2012: 12997735.53 Liu Z L, Zuo M J, Qin Y. Remaining useful life prediction of rolling element bearings based on health state assessment[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2016, 230(2): 314-330.54 Soualhi A, Razik H, Clerc G, et al. Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system[J]. IEEE Transactions on Industrial Electronics, 2016, 61(6): 2864-2874.55 Rabiei M, Modarres M. A recursive Bayesian framework for structural health management using online monitoring and periodic inspections[J]. Reliability Engineering & System Safety, 2017, 112: 154-164.56 宋传学, 肖峰, 刘思含, 等. 基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测[J]. 吉林大学学报: 工学版, 2016, 46(2): 333-339. Song Chuan-xue, Xiao Feng, Liu Si-han, et al. State observation of in-wheel motor-driven vehicles based on unscented Kalman filter[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(2): 333-339.57 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: 36-47.58 Jouin M, Gouriveau R, Hissel D, et al. Particle filter-based prognostics: review, discussion and perspectives[J]. Mechanical Systems and Signal Processing, 2016, 72/73: 2-31.59 Niu Q M, Liu F, Tong Q B, et al. Health condition assessment of ball bearings using TOSELM[J]. Journal of Vibroengineering, 2018, 20(1): 272-282.60 Guo L, Lei Y G, Li N P, et al. Machinery health indicator construction based on convolutional neural networks considering trend burr[J]. Neurocomputing, 2018, 292: 142-150.61 Liu T I, Jolley B. Tool condition monitoring(TCM) using neural networks[J]. International Journal of Advanced Manufacturing Technology, 2015, 78: 1999-2007.62 Ning C, Chen M Y, Zhou D H. Hidden markov model-based statistics pattern analysis for multimode process monitoring: an index-switching scheme[J]. Industrial & Engineering Chemistry Research, 2019, 53(27): 11084-11095.63 张继军, 马登武, 张金春. 基于HMM的电子设备状态监测与健康评估[J]. 系统工程与电子技术, 2013, 35(8): 1692-1696. Zhang Ji-jun, Ma Deng-wu, Zhang Jin-chun. Condition monitoring and health assessment of electronic equipment based on HMM[J]. Systems Engineering and Electronic Technology, 2013, 35(8): 1692-1696.64 许丽佳, 王厚军, 黄建国. CHMM在发射机状态监测与健康评估中的应用研究[J]. 电子科技大学学报, 2016, 39(6): 875-879, 890. Xu Li-jia, Wang Hou-jun, Huang Jian-guo. Application research of CHMM in transmitter condition monitoring and health assessment[J]. Journal of University of Electronic Science and Technology of China, 2016, 39(6): 875-879, 890.65 曾强, 黄政, 魏曙寰. 基于模糊理论和贝叶斯网络的燃气轮机健康状态评估方法[J]. 科学技术与工程, 2020, 20(11): 4363-4369. Zeng Qiang, Huang Zheng, Wei Shu-huan. Gas turbine health evaluation method based on fuzzy theory and Bayesian network[J]. Science Technology and Engineering, 2020, 20(11): 4363-4369.66 曾强, 黄政, 魏曙寰. 融合专家先验知识和单调性约束的贝叶斯网络参数学习方法[J]. 系统工程与电子技术, 2020, 42(3): 646-652. Zeng Qiang, Huang Zheng, Wei Shu-huan. Bayesian network parameter learning method combining expert prior knowledge and monotonic constraints[J]. Systems Engineering and Electronics, 2020, 42(3): 646-652.67 赵文清, 王强, 牛东晓. 基于贝叶斯网络的电抗器健康诊断[J]. 电力自动化设备, 2013, 33(1): 40-43. Zhao Wen-qing, Wang Qiang, Niu Dong-xiao. Reactor health diagnosis based on Bayesian network[J]. Electric Power Automation Equipment, 2013, 33(1): 40-43.68 Iamsumang C, Mosleh A, Modarres M. Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications[J]. Reliability Engineering & System Safety, 2018, 178: 118-129.69 Wang X H, Guo H Z, Wang J B, et al. Predicting the health status of an unmanned aerial vehicles data-link system based on a bayesian network[J]. Sensors, 2018, 18(11): 3916.70 么洪飞, 王宏健, 王莹, 等. 基于遗传算法DDBN参数学习的UUV威胁评估[J]. 哈尔滨工程大学学报, 2018, 39(12): 1972-1978. Mo Hong-fei, Wang Hong-jian, Wang Ying, et al. UUV threat assessment based on genetic algorithm DDBN parameter learning[J]. Journal of Harbin Engineering University, 2018, 39(12): 1972-1978.71 康守强, 王玉静, 崔历历, 等. 基于CFOA-MKHSVM的滚动轴承健康状态评估方法[J]. 仪器仪表学报, 2016, 37(9): 2029-2035. Kang Shou-qiang, Wang Yu-jing, Cui Li-li, et al. Evaluation method of rolling bearing health status based on CFOA-MKHSVM[J]. Chinese Journal of Scientific Instrument, 2016, 37(9): 2029-2035.72 Wang G F, Xie Q L, Zhang Y C. Tool condition monitoring system based on support vector machine and differential evolution optimization[J]. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 2017, 231(5): 805-813.73 Kong D D, Chen Y J, Li N, et al. Tool wear monitoring based on kernel principal component analysis and v-support vector regression[J]. International Journal of Advanced Manufacturing Technology, 2017, 89: 175-190.74 Sun C, Zhang Z S, Luo X, et al. Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing[J]. Journal of Sound and Vibration, 2016, 368: 249-263.75 武立群, 张亮亮. 基于数据挖掘技术的桥梁结构健康状态检测[J]. 吉林大学学报: 工学版, 2020, 50(2): 565-571. Wu Li-qun, Zhang Liang-liang. Bridge structure health detection based on data mining technology[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 565-571.76 院老虎, 连冬杉, 张亮, 等. 基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断[J]. 吉林大学学报: 工学版, 2021, 51(5): 1635-1641. Yuan Lao-hu, Lian Dong-shan, Zhang Liang, et al. Fault diagnosis of aircraft mechanical components based on densely connected convolutional networks and support vector machines[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.77 Xu G W, Liu M, Jiang Z F, et al. Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning[J]. Sensors, 2019, 19(5): 19051088.78 赵东, 臧雪柏, 赵宏伟. 基于果蝇优化的随机森林预测方法[J]. 吉林大学学报: 工学版, 2017, 47(2): 609-614. Zhao Dong, Zang Xue-bai, Zhao Hong-wei. Random forest prediction method based on fruit fly optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(2): 609-614.79 肖运启, 王昆朋, 贺贯举, 等. 基于趋势预测的大型风电机组运行状态模糊综合评价[J]. 中国电机工程学报, 2018, 34(13): 2132-2139. Xiao Yun-qi, Wang Kun-peng, He Guan-ju, et al. Fuzzy comprehensive evaluation of large-scale wind turbine operation status based on trend prediction[J]. Proceedings of the CSEE, 2018, 34(13): 2132-2139.80 Li H, Hu Y G, Yang C, et al. An improved fuzzy synthetic condition assessment of a wind turbine generator system[J]. International Journal of Electrical Power & Energy Systems, 2016, 45(1): 468-476.81 李鑫, 刘莹莹, 李赣华, 等. 基于模糊变权原理的卫星健康评估方法[J]. 系统工程与电子技术, 2017, 36(3): 476-480. Li Xin, Liu Ying-ying, Li Gan-hua, et al. Satellite health assessment method based on fuzzy variable weight principle[J]. Systems Engineering and Electronic Technology, 2017, 36(3): 476-480.82 国连玉, 李可军, 梁永亮, 等. 基于灰色模糊综合评判的高压断路器状态评估[J]. 电力自动化设备, 2014, 34(11): 161-167. Guo Lian-yu, Li Ke-jun, Liang Yong-liang, et al. Condition assessment of high-voltage circuit breakers based on grey fuzzy comprehensive evaluation[J]. Electric Power Automation Equipment, 2014, 34(11): 161-167.83 邱文昊, 黄考利, 连光耀, 等. 基于不确定性与重要度的改进DSmT健康状态评估[J]. 航空动力学报, 2017, 32(1): 96-104. Qiu Wen-hao, Huang Kao-li, Lian Guang-yao, et al. Improved DSmT health status assessment based on uncertainty and importance[J]. Journal of Aeronautical Dynamics, 2017, 32(1): 96-104.84 王亮, 吕卫民, 滕克难, 等. 基于Petri网的复杂设备健康状态退化分析[J]. 系统工程与电子技术, 2017, 36(10): 1973-1981. Wang Liang, Lv Wei-min, Teng Ke-nan, et al. Analysis of health state degradation of complex equipment based on petri nets[J]. Systems Engineering and Electronic Technology, 2017, 36(10): 1973-1981.85 Khan S, Yairi T. A review on the application of deep learning in system health management[J]. Mechanical Systems & Signal Processing, 2018, 107(1): 241-265.86 Lee J, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications[J]. Mechanical Systems & Signal Processing, 2016, 42(1/2): 314-334.
相关知识
锂离子电池安全状态评估研究进展
健康状态评估
保健饮料市场监测及发展趋势研究报告.doc
健康管理行业发展现状及前景趋势研究分析
区域健康发展力评估研究报告
电池寿命预测与健康状态评估技术研究
益生元研究现状及发展趋势
2024年健康养生行业现状及发展趋势预测
中国健康陶瓷产业发展现状及发展趋势
智慧健康监测设备行业发展趋势及市场现状分析
网址: 机电装备健康状态评估研究进展及发展趋势 https://m.trfsz.com/newsview1393589.html