## 4 Experiments
### Experiments and Evaluation Method  
Preparation. After the preprocessing of the data collected by various sensors in an A-class insulation panel production line, a complete time sequence signal dataset was established for training the CNN-LSTM network. Custom training and testing datasets acquired by above sensors are listed in Table 2. The running data of an A-class insulation panel production line at an average 16 h per day for 20 days were collected and preprocessed by a data filling algorithm. Data sampling rate was fixed at 1 kHz, and the mean of every 250 points was considered as a sample point. Therefore, the length of each sample in the custom multi-sensors dataset was 0.25 s, i.e., four samples per second. The collected dataset of the operation status of an A-class insulation board production line includes the normal operation status of the production line and five failure states: abnormal pressure of air compressor, failure or abnormality of cutting equipment, abnormal \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Device** & **Sampling Rate** & **Node Count** & **Range** \\ \hline
1 & Voltage sensors & 600 Hz & 3 & 0–450 V \\
2 & Current sensors & 450 Hz & 2 & 0–300 A \\
3 & Vibration & 500 Hz & 2 & 0–100 mm/s \\
4 & Rotating & 2 kHz & 1 & 0–20 kHz \\
5 & Pressure & 2400 Hz & 2 & 0–100 MPa \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of different sensors installed on the production line. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Length of Each Sample** & **Proportion** & **Number of Samples** \\ \hline Train & & 70\% & 3225.6 k \\ Validation & 0.25 s & 20\% & 921.6 k \\ Test & & 10\% & 460.8 k \\ All & - & - & 4608 k \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed description of the custom dataset. state of raw material mixing equipment, main motor failure, and spindle failure of stirring kettle, as listed in Table 3. Since serious data imbalance existed in the custom multiple-sensor dataset, data augmentation was applied to alleviate the influence of data imbalance. The data augmentation algorithm was mainly implemented by randomly cropping and inserting. Target data segment, i.e., data of abnormal state were randomly cropped and inserted into data of normal state. An abnormal pressure of the air compressor may lead to serious production accidents, and was one of the key monitoring nodes. Failure or abnormality of other equipment may cause serious quality problems of A-class thermal insulation boards. Therefore, a data augmentation algorithm was mainly applied at the time periods when abnormal appearance was observed. The ratio of normal to abnormal (air compressor, cutting equipment, mixing equipment and stirring kettle) state was 65.4:34.6 (8:9:3:8:3:9), which is close to 65:35. The original data captured from each sensor is displayed in Figure 6. These abnormal data were caused by electromagnetic interference, load change or other reasons, and may revert to normal in a short time, and should not be treated as faults. Hence, the original data should be preprocessed. Part of the preprocessed training data is shown in Figure 7. Considering the high-sensitivity of deployed sensors, the average value and overall trend of each sensor are helpful to train the proposed model and predict the state of the production line. To give a rough impression, subplots (a) and (b) refer to the starting moment, and subplots (c) to (f) reflect the normal running state of corresponding equipment. As can be seen from the figure, the preprocessed data have the same sampling frequency, which can effectively represent the operating status of each device. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Running State** & **Proportion** \\ \hline All devices & Normal & 68.7\% \\ Air compressor & Abnormal (pressure) & 1.8\% \\ Cutting equipment & Abnormal (voltage and current) & 8.4\% \\ Mixing equipment & Abnormal (rotation) & 9.3\% \\ Stirring kettle & Abnormal or breakdown (vibration) & 11.8\% \\ \hline \hline \end{tabular}
\end{table}