表性论文、专著（10项以内） [1] Zhou H, Wang B, Zio E, et al. Unsupervised Anomaly Detection of Machines Operating under Time-varying Conditions: DCD-VAE enabled Feature Disentanglement of Operating Conditions and States[J]. Reliability Engineering & System Safety, 2024: 110653.(中科院大类1区Top；JCR Q1区；IF: 9.4). [2] Zhou H, Huang X, Wen G, et al. Construction of health indicators for condition monitoring of rotating machinery: A review of the research[J]. Expert Systems with Applications, 2022: 117297. (中科院大类1区；JCR Q1区；IF: 8.5). [3] Zhou H, Huang X, Wen G, et al. Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions[J]. Mechanical Systems and Signal Processing, 2022, 173: 109050. (中科院大类1区Top；JCR Q1区；IF: 8.4). [4] Zhou H, Lei Z, Zio E, et al. Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions[J]. Mechanical Systems and Signal Processing, 2023, 191: 110139. (中科院大类1区Top；JCR Q1区；IF: 8.4). [5] Zhou H, Wang B, Zio E, et al. Hybrid system response model For Condition Monitoring of Bearings under Time-Varying Operating Conditions[J]. Reliability Engineering & System Safety, 2023: 109528. (中科院大类1区Top；JCR Q1区；IF: 9.4). [6] Zhou H, Wen G, Zhang Z, et al. Sparse dictionary analysis via structure frequency response spectrum model for weak bearing fault diagnosis[J]. Measurement, 2021, 174: 109010. (中科院大类2区；JCR Q1区；IF: 5.6). [7] Zhou H, Li H, Liu T, et al. A weak fault feature extraction of rolling element bearing based on attenuated cosine dictionaries and sparse feature sign search[J]. ISA transactions, 2020, 97: 143-154. (中科院小类1区Top；IF=7.3 ) [8] Zhou X, Zhou H, Wen G, et al. A hybrid denoising model using deep learning and sparse representation with application in bearing weak fault diagnosis[J]. Measurement, 2022, 189: 110633. (中科院大类2区；JCR Q1区；IF: 5.6). [9] Liu Z, Zhou H, Wen G, et al. A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions[J]. Measurement, 2023, 210: 112534. (中科院大类2区；JCR Q1区；IF: 5.6). [10] 周浩轩,温广瑞,黄鑫等.多尺度复合稀疏的齿轮箱复合故障诊断研究[J].振动.测试与诊断,2023,43(02):215-222+404.DOI:10.16450/j.cnki.issn.1004-6801.2023.02.002.（EI检索） |
专利及软件著作权登记（5项以内） [1] 温广瑞,周浩轩,苏宇,雷子豪,刘子岷,李良博,包渝锋,王恩秀,邓帅卿. 一种时变工况下的轴承运行健康监测方法、装置及设备[P] 陕西省：CN115219198A,2022-10-21. [2] 温广瑞,周浩轩,李良博,黄鑫,董书志,雷子豪,周鑫,张平. 一种齿轮箱复合故障成分分离诊断方法及装置[P]. 陕西省：CN113740055B,2022-08-09. [3] 温广瑞,董书志,周浩轩,黄鑫,雷子豪. 滚动轴承故障类型诊断方法、装置、设备及可读存储介质[P]. 陕西省：CN113740064A,2021-12-03. [4] 温广瑞,王恩秀,周浩轩,苏宇,刘子岷,田飞宇,张源麟. 轴承健康状态的监测方法、装置、设备及可读存储介质[P]. 陕西省：CN115510906A,2022-12-23 [5] 温广瑞,雷子豪,邓帅卿,周浩轩等. 一种预测轴承剩余使用寿命标签的构建方法、装置和设备[P]. 陕西省：CN116147917A,2023-05-23. |
