[1] 周东华, 胡艳艳. 动态系统的故障诊断技术[J]. 自动化学报, 2009, 35(6): 748-758. ZHOU D H, HU Y Y.Fault diagnosis techniques for dynamic systems[J]. Acta automatica sinica, 2009, 35(6): 748-758. [2] 李昂, 付敬奇, 沈华明, 等. 基于模糊聚类和猫群算法的室内定位算法[J]. 仪器仪表学报, 2020, 41(1): 185-194. LI A, FU J Q, SHEN H M, et al.Indoor positioning algorithm based on fuzzy clustering and cat swarm optimization[J]. Chinese journal of scientific instrument, 2020, 41(1): 185-194. [3] 张天瑞, 于天彪, 赵海峰, 等. 数据挖掘技术在全断面掘进机故障诊断中的应用[J]. 东北大学学报(自然科学版), 2015, 36(4): 527-531, 541. ZHANG T R, YU T B, ZHAO H F, et al.Application of data mining technology in fault diagnosis of tunnel boring machine[J]. Journal of Northeastern University (natural science), 2015, 36(4): 527-531, 541. [4] FAN H, SHAO S, ZHANG X, et al.Intelligent fault diagnosis of rolling bearing using FCM clustering of EMD-PWVD vibration images[J]. IEEE access, 2020, 8:145194-145206. [5] BESSA I V D, PALHARES R M, D'ANGELO M F S V, et al. Data-driven fault detection and isolation scheme for a wind turbine benchmark[J]. Renewable energy, 2016, 87:634-645. [6] LYU G, CHENG H, ZHAN H, et al.Fault diagnosis of power transformer based on multi-layer SVM classifier[J]. Proceedings of electric power system & automation, 2005, 75(1): 9-15. [7] 石鑫, 朱永利, 萨初日拉, 等. 基于深度信念网络的电力变压器故障分类建模[J]. 电力系统保护与控制, 2016, 44(1): 71-76. SHI X, ZHU Y L, SACHURILA, et al. Power transformer fault classifying model based on deep belief network[J]. Power system protection and control, 2016, 44(1): 71-76. [8] 张秦梫, 宋辉, 姜勇, 等. 基于OS-ELM的变压器局部放电模式识别[J]. 高电压技术, 2018, 44(4): 1122-1130. ZHANG Q Q, SONG H, JIANG Y, et al.Partial discharge pattern recognition of transformer based on OS-ELM[J]. High voltage engineering, 2018, 44(4): 1122-1130. [9] NOORI M, EFFATNEJAD R, HAJIHOSSEINI P.Using dissolved gas analysis results to detect and isolate the internal faults ofpower transformers by applying a fuzzy logic method[J]. IET generation, transmission & distribution, 2017, 11(10): 2721-2729. [10] 贾亚飞, 朱永利, 高佳程, 等. 基于样本加权FCM聚类的未知类别局部放电信号识别[J]. 电力自动化设备, 2018, 38(12): 107-112. JIA Y F, ZHU Y L, GAO J C,et al.Recognition of unknown partial discharge signals based on sample- weighted FCM clustering[J]. Electric power automation equipment, 2018, 38(12): 107-112. [11] 肖满生, 张龙信, 张晓丽, 等. 一种改进的区间型不确定数据模糊聚类方法[J]. 电子与信息学报, 2020, 42(8): 1968-1974. XIAO M S, ZHANG L X, ZHANG X L, et al.An improved fuzzy clustering method for interval uncertain data[J]. Journal of electronics and information technology, 2020, 42(8): 1968-1974. [12] 孙鹤旭, 孙泽贤, 张靖轩. 基于并行模糊C-均值聚类的风电机组发电机故障诊断研究[J]. 太阳能学报, 2020, 41(3): 8-14. SUN H X, SUN Z X, ZHANG J X.Fault diagnosis of wind turbine alternator based on parallel fuzzy c-means clustering algorithm[J]. Acta energiae solaris sinica, 2020, 41(3): 8-14. [13] CHUAN L, MARIELA C, DIEGO C, et al.A comparison of fuzzy clustering algorithms for bearing fault diagnosis[J]. Journal of intelligent and fuzzy systems, 2018, 34(6): 1-16. [14] WANG C, YANG H, MENG C.An efficient fault diagnosis strategy based on SVDD and fuzzy clustering for ground-based electronic equipment[C]//2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Dalian, China, 2019. [15] 尹爱军, 王昱, 戴宗贤, 等. 基于变分自编码器的轴承健康状态评估[J]. 振动·测试与诊断, 2020, 40(5): 1011-1016, 1030. YIN A J, WANG Y, DAI Z X, et al.Evaluation method of bearing health state based on variational auto encoder[J]. Vibration test and diagnosis, 2020, 40(5): 1011-1016, 1030. |