# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 4 The Case Study Phases and Results
### The Performance Measurement
The similar accuracy patterns observed in Models 2509 and 2557 might be attributed to their comparable dataset sizes (56 and 41 chunks, respectively). However, nuanced differences in precision, recall, and AUROC suggest that other factors, such as the inherent characteristics of the errors or the model architectures, contribute to their distinct performance. The slightly lower but reasonable accuracy of Models 2558 and 2559 corresponds to their smaller dataset sizes (12 and 37 chunks, respectively). Despite having fewer data entries, these models exhibit notable predictive capabilities, indicating effective learning from the available information. Generally, the correlation between the size of the dataset and model performance is evident, and larger datasets often lead to better results. However, the influence of other factors, such as error characteristics and the quality of the data, cannot be overlooked. The final step of the study involved merging the predictions of neural networks to accurately identify a single error event. To achieve this, the entire original dataset was processed and streamed independently to each neural network. By applying the same normalisation functions used during training and maintaining the same feeding window, \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy** & **Precision** & **Recall** & **AUROC** \\ \hline \(t+1\) & 0.96 & 0.05 & 0.92 & 0.59 \\ \(t+2\) & 0.96 & 0.05 & 0.94 & 0.57 \\ \(t+3\) & 0.96 & 0.05 & 1.00 & 0.54 \\ \(t+4\) & 0.96 & 0.043 & 0.89 & 0.52 \\ \(t+5\) & 0.96 & 0.04 & 0.95 & 0.56 \\ \(t+6\) & 0.96 & 0.05 & 1.00 & 0.60 \\ \(t+7\) & 0.95 & 0.06 & 0.95 & 0.62 \\ \(t+8\) & 0.95 & 0.05 & 0.96 & 0.61 \\ \(t+9\) & 0.95 & 0.05 & 0.93 & 0.63 \\ \(t+10\) & 0.95 & 0.05 & 0.93 & 0.63 \\ \hline \end{tabular}
\end{table} 
Table 11: Metric performances on the test set for Model 2559.  
the predictions were plotted at different levels. Part of the results of this process are presented in Figure 5. This should be read from the bottom to the top and from left to right to understand the behaviour of the signals. Each row represents a different time prediction frame: the top one shows the prediction results of the neural network closer to the current event, while the bottom depict those related to the one with the longer forecast. The total time frame taken into account is about a total of 300 TUs, and each prediction of the neural network is drawn with a different colour. Figure 5: The round table results of the five neural networks. The squared green signal represents the actual error that occurred during that specific time frame, and its value is labelled in the upper right corner. In addition, a threshold was set, represented as a horizontal dotted line, at 0.6 to highlight signals that exceed this value throughout the time frame. The expected ideal behaviour is that the neural network corresponding to the error reported on the top right of the squared signal (in this specific case, it is the 2509) should rise in advance and keep the same high value until the moment of the error occurrence. As the other neural networks are trained on different target classes, they should not show any activation. The row \(t+10\) shows that multiple neural networks seem to recognise an error event, including 2509 (coloured red). In row \(t+7\), the neural network for error 2558 stops its elicitation, as well as the neural network for error 2556 in row \(t+5\). Until row \(t+1\), the neural network 2509 confirms its prediction and extends its accuracy, intercepting the actual occurrence of the error. After this, it correctly stops the recognition, going back to a \"zero\" state.