## 4 Experiments  
### Data Preprocessing  
Data fill and timing alignment. Multiple sensors are installed in the fault diagnosis system of an A-class insulation panel production line for the mixing equipment, crawler Laminator and cutting equipment. Each sensor measures the corresponding key parameters, e.g., voltage, current or vibration signals, which are recorded in the form of time series. However, due to various reasons, the data collected by various sensors are missing on a small scale. In addition, because of different sensors, the data collected by different types of sensors has the problem of time inconsistency. Data loss and data synchronization affect the data quality, leading to a decrease in accuracy of prediction analysis. Therefore, it is necessary to perform the processing of data filling and time scale alignment for the collected original data. Data preprocessing is shown in Figure 5.  
To solve the problem of missing data values, when the missing data is small, the historical data of the previous moment (or the next moment) of the missing data can be used to make up the missing data by a filling or interpolation method. A data completion method based on quadratic interpolation can be introduced, since the missing data are few. A fitting curve by quadratic interpolation can estimate the overall trend of a small data gap. When there is a large amount of missing data, the above method is no longer applicable, so it is necessary to compare the data measured by sensors of adjacent measuring points or other relevant measuring points, and fill the missing data according to the data correlation. The preferred method is to find similar data at a similar time by data similarity analysis.  
Figure 4: The attention block for the proposed CNN-LSTM network.  
Figure 5: Acquisition and preprocessing of data fill and timing alignment.  
Principal component analysis (PCA) was used to perform data dimensionality reduction. The adjacent data whose similarity reached the threshold were used for data completion. If no similar data could be found, the entire segment of data with missing data were discarded. To solve the problem of time inconsistency of data collected by various sensors, it is necessary to transform the data. According to a uniform time interval, the mean value of all data values in the corresponding time period was taken to ensure the consistency of data collection frequency.  
Time series data selection. In this paper, the data measured by a variety of sensors on the production line of an A-class insulation board were predicted and studied. According to a large number of relevant works, the data obtained from the actual measurement of A-class insulation board were selected. The signals of the LSTM-based multi-feature fusion prediction model adopted in this paper consisted of five parts, i.e., voltage, current, vibration, rotation and pressure, as listed in Table 1.  
K-Nearest Neighbor algorithm. The output of the proposed CNN-LSTM model is a 10-dimension tensor, which can perform fault diagnosis by cascading a certain classification algorithm. Hence, K-Nearest Neighbor (K-NN) was introduced as the final classifier because of high accuracy and flexibility. The capacity of the custom multiple-sensor dataset is enormous, so, labeling the custom dataset is time-consuming and tedious. Therefore, the k-means algorithm was introduced to preprocess the custom dataset, and labeling and double-checking each cluster by experienced experts. The well-prepared custom dataset by k-means algorithm can be used as training dataset for the proposed CNN-LSTM model.