# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 4 Experiments and Analysis
### Results and Analysis
Prior to this analysis, the PCA-LSTM model was compared against traditional machine learning models, and it demonstrated superior performance in fault diagnosis due to its advanced feature extraction and sequence-modeling capabilities. Building on these promising results, the current study further evaluated the PCA-LSTM model by comparing it with the latest elevator fault diagnosis algorithm, IAO-XGBoost [5], to ascertain its relative effectiveness in handling various fault types. Figure 13 presents a comparative analysis of the PCA-LSTM model proposed in this study and the latest elevator fault diagnosis algorithm, IAO-XGBoost. The analysis tracked their accuracy over experimental steps for various fault types, including door-mechanism failure, overload-system error, speed-regulation malfunction, and signal-system error, using the same dataset for both models to ensure a fair comparison. The PCA-LSTM model consistently demonstrated higher accuracy in diagnosing mechanical and load-related faults, such as door-mechanism failure and overload-system error. This superior performance indicates the model's capability to extract relevant features and processing straightforward fault diagnostics. The PCA component effectively reduces data dimensionality while preserving essential information, and the LSTM component efficiently captures temporal dependencies, leading to enhanced fault prediction performance. Conversely, the IAO-XGBoost model excels in managing more dynamically complex faults, such as speed-regulation malfunction and signal-system error. Its ensemble learning framework allows it to adapt well to complex and irregular patterns, which is crucial for accurately diagnosing faults that involve rapid and unpredictable changes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Type**} & \multicolumn{3}{c}{**Classification Accuracy/\%**} & \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Testing Time**} \\ \cline{2-2} \cline{4-6}  & **Training Set** & **Validation Set** & **Test Set** & & **Iterations** \\ \hline RNN & 73.61 & 72.65 & 71.50 & 100 & 60.22 \\ LSTM & 80.23 & 83.56 & 82.47 & 100 & 126.32 \\ PCA-LSTM & 95.03 & 93.70 & 92.85 & 100 & 153.96 \\ CNN & 78.45 & 76.59 & 75.33 & 100 & 110.47 \\ SVM & 69.85 & 68.97 & 67.81 & 100 & 50.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different models. Using the same dataset for both models, this comparative analysis highlights the distinct strengths and limitations of each approach. The PCA-LSTM model shows its robustness and efficiency in handling faults with clear temporal patterns and consistent data characteristics, while the IAO-XGBoost model demonstrates its effectiveness in addressing faults with more variability and complexity. This detailed comparison provides valuable insights into the appropriate deployment of each model based on specific fault characteristics, thereby optimizing the overall elevator fault diagnosis system for improved reliability and efficiency. Deep belief networks (DBNs) [18] have been widely utilized in fault diagnosis due to their ability to learn hierarchical features from raw data, making them particularly effective in capturing complex and dynamic patterns in fault scenarios. The Figure 14 scatter plots above provide a comparative analysis between the PCA-LSTM model proposed in this study and the DBN model across four distinct elevator fault types: door-mechanism failure, overload-system error, speed-regulation malfunction, and signal-system error. Each subplot represents a specific fault type and compares the performance metrics of accuracy, precision, recall, and F1-score for both models. The PCA-LSTM model consistently outperforms the DBN model in diagnosing mechanical and load-related faults, such as door-mechanism failure and overload-system error, due to its superior capability in feature extraction and handling sequential data.