# Transfer Learning Strategies for Deep Learning-based PHM Algorithms
## 3 Transfer Strategies of Deep Learning PHM Algorithms  
The idea of transfer learning is to let a new algorithm inherit the knowledge (knowledge here represents the ability of existing algorithms to extract and analyze data features from the source domain) of the existing algorithm. Just as the teacher teaches the student knowledge, the higher level of summary knowledge transfer is undoubtedly the fastest and most efficient. Deep neural networks are based on network weights that come from information obtained from large amounts of data. If the weights in the existing neural network are extracted and transferred to the new neural network, it means that we \"transfer\" the learned features without having to retrain a network from scratch. Based on this idea, we regard the existing deep learning PHM algorithm as a pre-training model. When encountering a portable task, we transfer the network structure or weights of the existing algorithm and obtain a new deep learning PMM algorithm needed by the new task through very little training. The object in the transfer process has two parts: network structure and weights. According to whether the network weights are transferred during the transfer process, this paper proposes two types of deep learning PHM algorithm transfer strategy:  
TS1: Transfer both structure and weights of network simultaneously. TS2: Transfer only structure of network and initialize weights of network. The implementation process of the two strategies is shown in Figures 2 and 3. TS1 transfers the structure and weights of the network at the same time, so that the network in the target domain inherits more source domain information, which is suitable for scenarios where the target domain and source domain are similar. TS2 only transfers the network structure. Its weights need to be retrained using the target domain data after initialization. It inherits the network structure design experience of the source domain, but the inheritance in the knowledge layer is relatively small, which is more suitable for the scenarios where the target domain task is similar to the source domain but the sample similarity is low. Figure 2: TS1 Transfer both structure and weights of network simultaneously. These two types of transfer strategies can be used alone or in combination. For example, when transferring a diagnosis fault diagnosis algorithm based on a CNN network, three transfer methods can be designed according to the transfer tuple. When a CNN network is used for fault diagnosis, its structure generally consists of convolutional layers, pooling layers, fully connected layers, and a final classification layer. Among them, convolutional layers and pooling layers need to be connected sequentially, fully connected layers can be flexibly modified, and the classification layer needs to be placed at the end of the entire network structure. Convolution layers, pooling layers and full connection layers are mainly used for data feature extraction and compression. Transfer of these network layers can inherit the feature extraction and compression mode of source domain; classification layer is used to achieve accurate fault mode diagnosis, and different classification layers should be designed for different fault diagnosis tasks. Different layer types of transfer can produce different transfer effects. Therefore, based on TS1 and TS2, three transfer methods can be designed for CNN network used for fault diagnosis:  
TM1: Combining TS1 and TS2. Transfer the network structure of the convolutional and pooling layers of the source domain and their weights. The fully connected layer and classification layer only transfer the network structure, and then use the target domain data for retraining. TM2: TS1. Transfer all network structures and network weights in the source domain, and then use the target domain data for retraining. TM3: TS2. Transfer the source domain network structure, initialize the weights, and then use the target domain data for retraining. The application scenarios of the three transfer methods are analyzed.