# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 2 Classification of Health States of Power Converter
### Deep CNN Architecture  
The off-the-shelf deep CNN architecture ResNet [24] is used in this paper to build the transfer learning model for the purpose of classifying the snubber resistors' conditions in the power electronic devices used in the power converter for a three-phase BLDC motor drive.  
The ResNet architecture features more substantial depth compared to the previous state-off-the-art deep CNNs, e.g., VGG nets. It is known that the depths of the network plays a crucial role in the model performance. However, with the increase of the network depth further, the problem of vanishing/exploding gradients starts to saturate and even degrade the accuracy, and simply adding more layers leads to higher training errors and more difficulties to train and optimize.  
Figure 3: Spectrogram-based image obtained from one electrical cycle of the three-phase shunt voltage waveforms. (**a**) Normal condition; (**b**) when snubber resistance has degraded in \(S_{AH}\); (**c**) when snubber resistance has degraded in \(S_{AL}\); (**d**) when snubber resistance has degraded in \(S_{BH}\); (**e**) when snubber resistance has degraded in \(S_{BL}\); (**f**) when snubber resistance has degraded in \(S_{CH}\); (**g**) when snubber resistance has degraded in \(S_{CL}\).  
In order to solve the aforementioned problem in deep networks, the ResNet introduces a deep residual learning framework using shortcut connections. The shortcut connections can skip one or more layers, as shown in Figure 4. The shortcut connections perform identity mapping, and their outputs are added to the outputs of the stacked layers. Compared with the plain networks, the introduced shortcut connections neither bring about extra parameter nor computation complexity. The experiments in [24] revealed that, unlike the very deep plain networks, their proposed ResNets are able to gain accuracy and lower training error from increased depth. The 50/101/152-layer ResNets are much more accurate than the 34-layer ones, thanks to the largely increased network depth. Besides, despite the ResNet being substantially deeper than the previous VGG nets, the complexity of the former one is still lower, and the FLOPs of the 34-layer ResNet is only 18% of that of VGG-19.  
In this paper, the 50-layer ResNet is used, as shown in Figure 5. The last fully connected (FC) layer of ResNet50 is replaced with a new FC layer with a size of 7 to suit the requirement to classify seven snubber resistor conditions. The deep network is trained offline in a personal computer using the collected dataset. During the operation of the BLDC drive system, the voltages of the three shunt resistors are measured and used in the current control scheme. These voltage waveforms are also used in the online monitoring of the degradation of the snubber resistors. The embedded motor controller converts the three-phase waveforms into either greyscale or spectrogram image, loads the trained network into the memory, and performs the classification task online. An overview of the monitoring system is shown in Figure 6. Our purpose here is to give a clear guidance for when to start a system maintenance (typically once over several months), and the monitoring does not need to be in real time.  
Figure 4: Shortcut connection.  
Figure 5: Architecture of ResNet50.