## 5 Discussions and Future Works  
(1) Modeling Based on PE  
Vibration data have the characteristics of high dimensionality, nonlinearity, and diversity. Signal processing is the core of the PEM layer. Other signal processing methods, including empirical mode decomposition, local mean decomposition, and Hilbert-Huang transform, can extract discriminative features from different domains, and those feature extraction methods are worth studying. In the PEM layer, the question of how to extract valuable features from noisy data and extract a sufficient number of features from imbalanced data are two problems that need to be considered. CTC often operates under harsh working environments, and the vibration signal collected often contains irregular noise. Feature information can easily be annihilated by strong background noise. At the same time, it was found that the distribution between each category of samples was imbalanced in our experiment. In the context of CPS hybrid modeling, there is still a great amount of progress to be made in the feature extraction of noisy and imbalanced data.  
(2) Modeling Based on data  
Whether the data-driven CPS framework can be successfully deployed in the SPL depends on the quality of the datasets used. The more labeled multi-sensor data are available, the better the accuracy will be. In fact, the use of data with less labels limits the application of DL in the CPS. The other problem is represented by limited data. A major challenge in this CPS framework is in obtaining a sufficient amount of training data, which is a time-consuming, laborious, and costly process. The use of semi-supervised or unsupervised DL for less-labeled data and few-shot learning with a meta-learning paradigm for limited data are techniques that have been widely used in the field of computer vision. These methods are worth using in attempts to solve the above problems.  
Figure 12: Confusion matrix of bearing datasets. (**a**) LeNet. (**b**) CNN. (**c**) LSTM. (**d**) BiLSTM. (**e**) MSCNN. (**f**) ResNet18.  
(3) Integrating digital twin with CPS  
CPS improves the communication between physical and cyber space. In a digital twin, a high-fidelity digital copy is built through big data and physical models in physical systems to provide services for CPS monitoring, analysis, decision making, and feedback to physical entities. The question of how to integrate digital twins with CPS for the condition monitoring of CTC in SPL would be an interesting research direction to explore in the future.  
(4) Infusing 5G into CPS  
Various industrial Ethernet and fieldbus technologies are still the main data transmission technologies used in CPSs. Some wireless transmission technologies, such as Bluetooth and ZigBe, etc., have also been applied to CPS. 5G promotes the development of wireless communication technology. Its main features are enhanced mobile broadband, low latency, high reliability, and mass machine communication. 5G can effectively meet the needs of CPS for large-scale data acquisition and sensing, precise control, remote processing. The question of how to deploy 5G in all layers of CPS, especially in the smart connection layer, should be addressed in future research.