# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Manufacturing CPS
#### 4.2.4 Predictive Maintenance  
[PERSON] et al. [74] proposed a convolutional network model that mines deterioration information in order to anticipate the remaining usable life of a machine. Their designed scheme models the sensor network by taking into account both the spatial-temporal dependencies of the sensors. It adopts a hierarchical graph representation layer to model spatial dependencies, a bi-directional LSTM to model temporal dependencies, as well as a regularized self-attention graph pooling for effective information fusion. [PERSON] et al. [75] proposed SuperGraph, a feature extraction technique for diagnosing rotating machinery faults. The technique adopts graph theory-based spectrum analysis so that a spatial-temporal graph can be constructed and a Laplacian matrix-based feature vector can be derived. GCN was further utilized to learn the latent features. [PERSON] et al. [77] proposed a hybrid multi-task learning framework by integrating CNN and LSTM to reflect the relatedness of functional life prediction with the health status detection process for complex multi-object systems in the CPS environment. The CNN extracts significant spatial-temporal features from raw multi-sensory input data and compresses the condition monitoring data, while the LSTM captures the temporal dependencies. As another example, [PERSON] et al. [76] proposed an equipment fault prediction technique using spatial-temporal graph information. Their proposed scheme has the potential to stop fatal damage and reduce equipment maintenance costs. Their experimental results showed that their approach is capable of offering precise short-term and long-term fault prediction. These efforts can be mapped to the cube \(<\)\(X_{2}\), \(Y_{3}\), \(Z_{2}\), and \(Z_{3}\)\(>\) as shown in Figure 3 and Table 3.  
There are other related efforts concerning predictive maintenance. For instance, [PERSON] et al. [78] discussed the importance of human-robot collaboration (HRC) in smart manufacturing processes and the role of human action recognition in enabling HRC. In their study, a method based on optical flow and CNN transfer learning was proposed. Their proposed scheme leverages the optical flow to extract time-related information from video images and simultaneously parse spatial-temporal information with a two-stream CNN structure. Transfer learning was also leveraged to establish feature extraction capability by pre-training the model on a non-manufacturing specific dataset and transferring the gained knowledge to the target domain of assembly tasks, which have limited training samples. [PERSON] et al. [79] addressed the problems of scene recognition in underground coal mining using CNN, LSTM, and an attention mechanism. [PERSON] et al. [80] proposed a data-driven method using a graph convolution network to model the compound and time-varying characteristics of the process industry. The technique tends to capture the relationships among variables. The model was trained with regularization terms so that distinctive localized spatial-temporal correlations can be learned and time-series properties can be derived using temporal convolution. Furthermore, [PERSON] et al. [81] proposed CLSTMA, a hybrid model that integrates CNN, LSTM, and an attention mechanism to monitor water quality in a wastewater treatment system. In their proposed model, a sequential fusion CNN, LSTM, and attention mechanism were used to predict water quality and assist in the reduction of energy and emissions. Their proposed scheme captures the fused spatial features using CNN, LSTM for the temporal information, and variable-weighted calculations using the attention mechanism. Likewise, [PERSON] et al. [82] employed historical energy consumption time series and previous knowledge of material flow to propose a spatial-temporal deep learning network (STDLN) framework, which merges a GCN and a GRU and forecasts the energy consumption of nodes. In order to enhance maintenance practices in production CPS, [PERSON] et al.