基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断

[1] 基于深度学习理论的机械装备大数据健康监测方法 [J]. 机械工程学报, 2015, 51 (21) : 49 - 56

[2] 基于粒子群优化的最小二乘支持向量机在时间序列预测中的应用 [J]. 中国机械工程, 2011, 22 (21) : 2572 - 2576

[3] 多特征信息融合的贝叶斯网络故障诊断方法研究 [J]. 中国机械工程, 2010, 21 (08) : 940 - 945+967

[4] 基于混合智能新模型的故障诊断 [J]. 机械工程学报, 2008, (07) : 112 - 117

[5] 基于小波分析的旋转机械故障诊断技术及其试验研究 [J]. 中国机械工程, 2006, (S2) : 328 - 331

[6] Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data.[J].Feng Jia;Yaguo Lei;Jing Lin;Xin Zhou;Na Lu.Mechanical Systems and Signal Processing.2016,

[7] Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings.[J].Meng Gan;Cong Wang;Chang׳an Zhu.Mechanical Systems and Signal Processing.2016,

[8] Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals.[J].Chuan Li;René-Vinicio Sanchez;Grover Zurita;Mariela Cerrada;Diego Cabrera;Rafael E. Vásquez.Mechanical Systems and Signal Processing.2016,

[9] Deep learning of support vector machines with class probability output networks.[J].Sangwook Kim;Zhibin Yu;Rhee Man Kil;Minho Lee.Neural Networks.2015,

[10] An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (09) : 4113 - 4122

[1] 基于深度学习理论的机械装备大数据健康监测方法 [J]. 机械工程学报, 2015, 51 (21) : 49 - 56

[2] 基于粒子群优化的最小二乘支持向量机在时间序列预测中的应用 [J]. 中国机械工程, 2011, 22 (21) : 2572 - 2576

[3] 多特征信息融合的贝叶斯网络故障诊断方法研究 [J]. 中国机械工程, 2010, 21 (08) : 940 - 945+967

[4] 基于混合智能新模型的故障诊断 [J]. 机械工程学报, 2008, (07) : 112 - 117

[5] 基于小波分析的旋转机械故障诊断技术及其试验研究 [J]. 中国机械工程, 2006, (S2) : 328 - 331

[6] Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data.[J].Feng Jia;Yaguo Lei;Jing Lin;Xin Zhou;Na Lu.Mechanical Systems and Signal Processing.2016,

[7] Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings.[J].Meng Gan;Cong Wang;Chang׳an Zhu.Mechanical Systems and Signal Processing.2016,

[8] Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals.[J].Chuan Li;René-Vinicio Sanchez;Grover Zurita;Mariela Cerrada;Diego Cabrera;Rafael E. Vásquez.Mechanical Systems and Signal Processing.2016,

[9] Deep learning of support vector machines with class probability output networks.[J].Sangwook Kim;Zhibin Yu;Rhee Man Kil;Minho Lee.Neural Networks.2015,

[10] An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (09) : 4113 - 4122