Sifang Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sifang Zhao has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Mingsheng Wang
College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
1 shared publication - Qiang Song
Beijing Institute of Technology
1 shared publication - Wuxuan Lai
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China
1 shared publication
Latest Publications
- Open Access
Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM
The diagnosis of an inter-turn short circuit (ITSC) fault at its early stage is very important in permanent magnet synchronous motors as these faults can lead to disastrous results. In this paper, a multiscale kernel-based residual convolutional neural network (CNN) algorithm is proposed for the diagnosis of ITSC faults. The contributions are majorly located on two sides. Firstly, a residual learning connection is embedded into a dilated CNN to overcome the defects of the conventional convolution and the degradation problem of a deep network. Secondly, a multiscale kernel algorithm is added to a residual dilated CNN architecture to extract high-dimension features from the collected current signals under complex operating conditions and electromagnetic interference. A motor fault experiment with both constant operating conditions and dynamics was conducted by setting the fault severity of the ITSC fault to 17 levels. Comparison with five other algorithms demonstrated the effectiveness of the proposed algorithm.
Open AccessMultiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM
The diagnosis of an inter-turn short circuit (ITSC) fault at its early stage is very important in permanent magnet synchronous motors as these faults can lead to disastrous results. In this paper, a multiscale kernel-based residual convolutional neural network (CNN) algorithm is proposed for the diagnosis of ITSC faults. The contributions are majorly located on two sides. Firstly, a residual learning connection is embedded into a dilated CNN to overcome the defects of the conventional convolution and the degradation problem of a deep network. Secondly, a multiscale kernel algorithm is added to a residual dilated CNN architecture to extract high-dimension features from the collected current signals under complex operating conditions and electromagnetic interference. A motor fault experiment with both constant operating conditions and dynamics was conducted by setting the fault severity of the ITSC fault to 17 levels. Comparison with five other algorithms demonstrated the effectiveness of the proposed algorithm.