·790· 智能系统学报 第16卷 3)研究了4种故障知识的获得和特性，及基 39(6):200-205 础故障意识、故障情感和故障理智的特点和应 [8]徐红辉，王翀，范杰.基于故障状态演化的高速公路机电 用。认为人工智能系统故障分析原理是基于信息 设备智能维护系统设计[).现代电子技术，2019,42(24)： 生态方法论，考虑了基础故障意识、故障情感与 112-115. 故障理智，及故障语义信息的综合安全决策与降 XU Honghui,WANG Chong,FAN Jie.Design of fault 低故障反应生成过程，目的是确保系统在规定条 state evolution based intelligent maintenance system for 件下完成预定功能。 electromechanical equipments on expressway[J].Modern electronics technique,2019,42(24):112-115. 参考文献： [9]崔铁军，李莎莎.安全科学中的故障信息转换定 律[.智能系统学报，2020,15(2)：360-366. [1]CUI Tiejun,LI Shasha.Deep learning of system reliability CUI Tiejun,LI Shasha.The conversion law of fault in- under multi-factor influence based on space fault tree[J]. formation in Safety Science[J].CAAI transactions on intel- Neural computing and applications,2019,31(9): ligent systems,2020,15(2):360-366 4761-4776. [10]范土雄，李立新，王松岩，等.人工智能技术在电网调控 [2]崔铁军，马云东.多维空间故障树构建及应用研究).中 中的应用研究.电网技术2020,44(2)：401-411 国安全科学学报，2013.23(4)：32-37. FAN Shixiong,LI Lixin,WANG Songyan,et al.Applica- CUI Tiejun,MA Yundong.Research on multi-dimension- tion analysis and exploration of artificial intelligence tech- al space fault tree construction and application[J].China nology in power grid dispatch and control[J].Power sys- safety science journal,2013,23(4):32-37. tem technology,2020,44(2):401-411. [3]张豫龙，王建峰，李陶然，等.基于观测图像识别的天文 望远镜智能故障辅助诊断系统[，天文研究与技术， [11]盛海华，王德林，马伟，等.基于大数据的继电保护智能 运行管控体系探索.电力系统保护与控制，2019， 2020,17(3):392-398. 47(22:168-175. ZHANG Yulong,WANG Jianfeng,LI Taoran,et al.The SHENG Haihua,WANG Delin,MA Wei,et al.Explora- intelligent fault auxiliary diagnosis system of astronomical telescope based on observation image recognition[J].As- tion of intelligent operation management system of relay tronomical research&technology,2020,17(3):392-398. protection based on big data[J].Power system protection [4]贾惠彬，盖永贺，李保罡，等.基于强化学习的电力通信 and control,2019,47(22):168-175. 网故障恢复方法[】.中国电力，2020,53(6)：34-40 [12]王春影.低温环境下汽车发动机运行故障智能诊断仿 JIA Huibin,GAI Yonghe,LI Baogang,et al.Power com- 真).计算机仿真，2018,35(12：131-134 munication network recovery from large-scale failures WANG Chunying.Intelligent diagnosis of automobile en- based on reinforcement learning[J].Electric power,2020, gine running fault in low temperature environment[J]. 53(6):34-40. Computer simulation,2018,35(12):131-134. 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[4] 尹相国, 张文, 路致远, 等. 面向智能变电站二次设备的 故障诊断方法研究 [J]. 电测与仪表, 2020, 57(3): 39–45. YIN Xiangguo, ZHANG Wen, LU Zhiyuan, et al. Re￾search on fault diagnosis method for secondary equipment of intelligent substation[J]. Electrical measurement & in￾strumentation, 2020, 57(3): 39–45. [5] 高凯, 宋娜, 王红艳, 等. 基于大数据的地铁车辆智能故 障监测系统研究 [J]. 铁道机车车辆, 2019, 39(S1): 35–39. GAO Kai, SONG Na, WANG Hongyan, et al. Research on intelligent fault monitoring system for metro vehicles based on big data[J]. Railway locomotive & car, 2019, 39(S1): 35–39. [6] 张龙, 吴荣真, 雷兵, 等. 基于多尺度熵的滚动轴承故障 可拓智能识别 [J]. 噪声与振动控制, 2019, 39(6): 200–205. ZHANG Long, WU Rongzhen, LEI Bing, et al. Extensible intelligent identification for rolling bearing faults using multiscale entropy[J]. Noise and vibration control, 2019, [7] 39(6): 200–205. 徐红辉, 王翀, 范杰. 基于故障状态演化的高速公路机电 设备智能维护系统设计 [J]. 现代电子技术, 2019, 42(24): 112–115. XU Honghui, WANG Chong, FAN Jie. 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[11] 王春影. 低温环境下汽车发动机运行故障智能诊断仿 真 [J]. 计算机仿真, 2018, 35(12): 131–134. WANG Chunying. Intelligent diagnosis of automobile en￾gine running fault in low temperature environment[J]. Computer simulation, 2018, 35(12): 131–134. [12] BUKKAPATNAM S T S, AFRIN K, DAVE D, et al. Ma￾chine learning and AI for long-term fault prognosis in complex manufacturing systems[J]. CIRP annals, 2019, 68(1): 459–462. [13] WANG Shiqiang, XING Jianchun, JIANG Ziyan, et al. A novel sensors fault detection and self-correction method for HVAC systems using decentralized swarm intelli￾gence algorithm[J]. International journal of refrigeration, 2019, 106: 54–65. [14] NASIRI A, TAHERI-GARAVAND A, OMID M, et al. Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images[J]. Ap￾plied thermal engineering, 2019, 163(1): 114410. [15] KUNCAN M, KAPLAN K, MINAZ M R, et al. A novel feature extraction method for bearing fault classification with one dimensional ternary patterns[J]. ISA transac￾tions, 2020, 100: 346–357. [16] [17] BENSAOUCHA S, BESSEDIK S A, AMEUR A, et al. ·790· 智 能 系 统 学 报 第 16 卷