# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 4 Experiments and Analysis
### Results and Analysis  
By applying Principal Component Analysis (PCA) to the time series data representing various operating states of the elevator, we successfully extracted key features and fed these principal components into a Long Short-Term Memory (LSTM) model. This approach  
Figure 10: Training and validation loss trends for PCA-LSTM elevator fault prediction model. Figure 9: Performance of PCA-LSTM model in predicting elevator speed and fault detection. significantly enhanced the model's accuracy in predicting elevator faults, particularly in differentiating between current and vibration signals. We observed that the current data, optimized through PCA (including the rated and starting currents), demonstrated high classification accuracy across all four fault types. Although the average acceleration and operational noise had weaker recognition capabilities, especially in identifying elevator door opening and closing faults, the integration of PCA and LSTM still allowed the model to achieve the anticipated objectives. This combined strategy underscores the importance of fully utilizing different data signals during the model training and validation process, thereby enhancing the model's robustness. Figure 11 presents the training metrics of the PCA-LSTM model, represented by the ROC curve with an area under the curve (AUC) of 0.88. This indicates that the PCA-LSTM model performs well in the context of elevator fault diagnosis. An AUC value of 0.88 demonstrates that the model has a high level of accuracy and reliability in distinguishing between normal operation and faults in elevators. Particularly when dealing with various types of fault data, the model effectively reduces the instances of false positives and false negatives. This capability ensures that the PCA-LSTM model can be a robust tool for minimizing diagnostic errors and enhancing the safety and maintenance protocols for elevator systems. Table 4 displays the performance of different principal components (PCA1, PCA2, and PCA3) in elevator fault prediction after applying Principal Component Analysis (PCA). It lists the overall classification accuracy, accuracy in the normal state, and accuracies for specific failures such as open-door failure, closed-door failure, and brake failure, along with the number of iterations. For instance, PCA1 achieved an overall classification accuracy of 83.61%, with 90.36% accuracy in normal conditions, 83.92% for open-door failures, 81.65% for closed-door failures, and 88.82% for brake failures, with each principal component undergoing 100 iterations. These metrics assist in evaluating the impact of different principal components on the model's performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Principal** & **Classification** & **Normal-State** & **Open-Door-Failure** & **Closed-Door-Failure** & **Brake-Failure** & **Number of** \\
**Component** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Iterations** \\ \hline PCA1 & 83.61 & 90.36 & 83.92 & 81.65 & 88.82 & 100 \\ PCA2 & 90.23 & 90.87 & 85.22 & 72.56 & 89.64 & 100 \\ PCA3 & 82.25 & 88.55 & 71.33 & 73.52 & 93.21 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of Principal Component Analysis in elevator fault prediction. Figure 11: ROC curve of the PCA-LSTM model. To evaluate the performance of the proposed models, we applied Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM) models to the same dataset in order to predict elevator failures. The initial learning rate and batch size for the models were set to 0.001 and 64, respectively, with the number of iterations capped at 100. We utilized the validation set to assess the training effectiveness of the models, and we fine-tuned the model parameters based on the validation outcomes. A comparison of the classification accuracy of different models on the validation set reveals that our PCA-LSTM network achieves significantly higher classification accuracy than the conventional networks.