稀疏自动编码(Sparse Auto Encoder, SAE)通过寻找一组"超完备"基向量用于挖掘输入数据的内在结构与模式,使得高层输出能够更好的表达输入样本的类别信息,其良好的降维性能受到广泛关注并逐渐应用在机械设备故障诊断中。然而,...稀疏自动编码(Sparse Auto Encoder, SAE)通过寻找一组"超完备"基向量用于挖掘输入数据的内在结构与模式,使得高层输出能够更好的表达输入样本的类别信息,其良好的降维性能受到广泛关注并逐渐应用在机械设备故障诊断中。然而,SAE模型中隐含层特征数直接影响高层输出对低层输入模式的表达效果,简单的设置隐含层特征数难以取得理想的识别效果,针对该问题,利用萤火虫寻优算法的优点,确定各个隐含层的最优特征数,从而确定最优的SAE模型。轴承仿真及故障状态识别实验证明,隐含层特征数确定之后的稀疏自动编码模型在不同测试样本数目下均能取得比浅层结构及随机参数SAE模型更好的识别效果,得到更高的识别正确率。展开更多
Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation...Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.展开更多
文摘稀疏自动编码(Sparse Auto Encoder, SAE)通过寻找一组"超完备"基向量用于挖掘输入数据的内在结构与模式,使得高层输出能够更好的表达输入样本的类别信息,其良好的降维性能受到广泛关注并逐渐应用在机械设备故障诊断中。然而,SAE模型中隐含层特征数直接影响高层输出对低层输入模式的表达效果,简单的设置隐含层特征数难以取得理想的识别效果,针对该问题,利用萤火虫寻优算法的优点,确定各个隐含层的最优特征数,从而确定最优的SAE模型。轴承仿真及故障状态识别实验证明,隐含层特征数确定之后的稀疏自动编码模型在不同测试样本数目下均能取得比浅层结构及随机参数SAE模型更好的识别效果,得到更高的识别正确率。
文摘Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.
