以下是一些利用仿真信号训练神经网络进行旋转机械故障特征提取的论文：
“Fault diagnosis of rotating machinery based on vibration signal analysis using wavelet packet decomposition and artificial neural network” by H. Wang, Y. Fan, and X. Liu.
“Rolling element bearing fault diagnosis using adaptive resonance theory neural networks and Hilbert-Huang transform” by S. M. Karimian, A. Shojaei, and A. Ahmadi.
“A comparative study of machine learning methods for detection of rolling element bearing faults in induction motors” by T. Verma, K.D.Patel, R.Agrawal
“Fault diagnosis of bearings using a hybrid approach combining deep belief networks and grey wolf optimization algorithm” by Q.Yang,Y.Wu,Z.Li,S.Zhou
5.“Feature Extraction Based on Improved EMD and Deep Belief Network for Fault Diagnosis of Bearings Under Varying Speed Conditions” by D.Zhang,Q.Gao,H.Wang
6.“A Novel Hybrid Feature Extraction Method for Rolling Bearing Condition Monitoring Based on the Variational Mode Decomposition and Convolutional Neural Networks” by Z.Xue,X.Jia,L.Li,Q.Wang
7.“Rotating Machinery Fault Diagnosis Using Gabor Filter-Based Mel Frequency Cepstral Coefficients with Artificial Neural Network Algorithm” by M.Chandrasekaran,V.Kumarappan,S.Srinivasan,K.Muruganandam
这些论文可以在学术数据库中进行查找和阅读，以深入了解如何使用仿真信号训练神经网络进行旋转机械故障特征提取。
![驱动代码
void kalRxTaskletSchedule(struct GLUE_INFO *pr)
{
static unsigned int num = 0;
tasklet_hi_schedule(&pr->rRxTask[(num++)%NR_CPUS]);
// tasklet_hi_schedule(&pr->rRxTask);
DBGLOG(HAL, ERROR,](https://linuxcpp.0voice.com/zb_users/upload/2023/05/202305162226144313964.png)
