## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of CPS** & **Layer** \\
**Framework Layers** & [2; 3; 5; 8; 9; 10; 11; 12]: 5C framework \\ \hline five levels & [13]: machine, data analysis, optimization, design layer \\ \hline four levels & [6]: physical, network, virtual, application \\  & [14]: connection, features, mining and modeling, \\  & decision \\ \hline three levels & [7]: physical connection, middleware, computation \\  & [15]: physical resources, local server, cloud server \\ \hline Unclear delineated & [16; 17; 18; 19; 20; 21] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The division of CPS layers. \begin{table}
\begin{tabular}{c c} \hline \hline
**CPS Application Area** & **CPS Algorithm Implementation** \\ \hline Workshop management system: [7; 8] & Shallow ML: [2; 3; 8; 9; 10] \\ IM: [5; 11; 12; 13] & \\ Biomedicine: [9; 18] & \\ Welding: [2; 3] & \\ PHM: [6; 10; 15; 16; 21] & \\ Production optimization: [17] & \\ Intelligent video monitoring system: [19] & \\ Transportation: [14] & \\ Object Detection: [20] & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The application area and algorithm implementation of CPS. for failure proportions of 27%, 41%, and 20%, respectively [28]. [PERSON] et al. [29] identified motors to be a vital component of the production line and showed that reliability analyses can ensure the reliable operation of the overall security of the SPL, enabling it to avoid economic losses and catastrophic accidents. [PERSON] et al. [16] proposed a way to change from preventive maintenance to predictive in cyber-physical production systems and introduced a new DL model for estimating the remaining useful life (RUL) of the monitored equipment. A new automatic RUL prediction method for continuous production line equipment based on ML is proposed in [30]. [PERSON] et al. [31] developed a data-driven PHM for potential failures in production lines. [PERSON] et al. [32] performed a systematic literature review of ML applications in production lines, where quality optimization, scheduling optimization, yield improvement, and product failure detection are the main research fields. DL technology has obtained significant achievements in SPL applications. There is great potential to realize the condition monitoring of CTC by integrating DL technology into a CPS architecture. Within the background of network collaborative manufacturing, the integration of cyber and physical resources of SPL is further deepened and the condition monitoring of CTC has become a hot area in academia and industry. Some research gaps could be identified after reviewing the literature: (1) The research focuses on quality control, production scheduling, yield improvement, etc. The research on the condition monitoring of CTC in SPL is limited, especially that based on the data of motor spindles in the production line of machine tools. (2) There is a lack of a unified CPS framework and algorithm implementation that combines emerging technologies such as Internet of Things, big data, DL, etc., for CTC condition monitoring and reliability analysis in the manufacturing process of SPL. (3) The existing framework does not consider the key technical characteristics of CPS, especially those of deploying a CPS framework from the perspective of hybrid modeling. In this paper, we proposed a theoretical CPS framework for CTC condition monitoring and reliability analysis, which is distinguished from existing solutions. Hybrid modeling, detailed algorithm implementation, and integrating DL rather than shallow ML are the main characteristics of the CPS framework. The accurate mapping of the vibration signal, which is easy to collect, and the CTC condition, which is difficult to obtain, was established. The effectiveness and predictive ability of the framework were validated with the use of multiple DL algorithms and signal processing steps through experiments on three datasets. The framework and approach proposed in this paper can be generalized to other SPL big data scenarios.