## 2 CPS Architecture for CTC Condition Monitoring  
CPS is a computer-based technical system that has a clear architecture and sequential workflow manner that closely connects various complex processes and information from physical reality with that from cyber space, providing computation, communication, and control. Inspired by the layer architecture design of CPS, industrial big data, Internet of Things, and DL, this paper proposes a novel five-layer CPS framework which consists of the following layers: a smart connection layer, physics equation-based modeling (PEM) layer, ML-based modeling (MLM) layer, cognition layer, and configuration layer. The detailed architecture is shown in Figure 1, which presents the integration of physical components (e.g., motors, spindles, bearings, gears, and sensors) and cyber components (e.g., communication, computing, control). Smart Connection Layer: data acquisition is the main function of this layer. Sensors are the sensing elements of physical systems. Through reasonable multi-sensor position arrangement and type selection [33], variable accurate and reliable signals, such as vibration, current, force, and rotation error signals, can be collected. The collected datasets are transmitted to a large-capacity data storage device and saved in the cloud. Two important problems must be considered at this layer. Firstly, the type and specification of the sensor ensures the validity of the data, meaning whether the data are reliable. Secondly, the location of the sensor is related to the accuracy of the DL model, which relates to whether the data can be used to make accurate predictions. PEM Layer: CPS modeling based on physical equations is the core technology used in this layer. The raw data are transferred to this layer through fieldbus and/or industrial Ethernet. Raw data are heterogeneous, imperfect, and large-scale [34] and cannot be directly used in the next layer. Data preprocessing, including data fusion, data normalization, and feature extraction, are the important considerations at this level. Through this layer, low-value data can be transformed into high-value information. The detailed physical modeling workflow will be discussed in Section 3.2. MLM Layer: The meaningful information obtained from the PEM Layer is transferred to this level to build a CPS data-driven model, and a workflow from the physical components to the cyber components is established. This layer needs to provide support for the development of DL algorithms, the prediction model construction, and transfer learning and application implementation in a cyber virtual environment. Historical vibration data  
Figure 1: CPS framework for CTC condition monitoring and reliability analysis. will be used to develop and train the ML predictive model off-line, while real-time data are fed into the pretraining model to achieve online prediction. In practical applications, the problem of new datasets or tasks will be encountered; transfer learning can effectively solve this problem of models requiring training from scratch. The essence of this method is to solve new but similar tasks by applying knowledge that has been learned previously. Cognition Layer: The real-time condition monitoring data of CTC and the prediction results from the MLM Layer can be displayed to experts through visualization techniques such as histograms and confusion matrices. In addition, the meaningful prediction results can be further analyzed and mined to evaluate the real-time equipment state. This layer informs us of the optimal decisions to be taken. Configuration Layer: The core purpose of this layer is to transform the decision information of the cognition layer into maintenance activities and realize supervisory control from physical components to cyber components and back to physical components. The required PHM actions, such as rotation error compensation, replace the bearing, and gear maintenance, can be taken.