## 4 Experiments
### Experimental Results
#### 4.4.5 Model Comparision Result  
In the evaluation of different models for fault prediction, we considered several classical sequence models, including LSTM, RNN, and Transformer. These models each possess distinct advantages and characteristics. LSTM is specialized in addressing long-term dependency issues, successfully overcoming the shortcomings of the original recurrent neural networks through gating mechanisms, thereby exhibiting excellent performance in processing sequential data [38]. The RNN, on the other hand, demonstrates its capability in handling time series data by memorizing previous feature representations [39]. Meanwhile, the Transformer model leverages a multihead attention mechanism to effectively model both long-term and short-term temporal features, granting it an advantage in time series processing [40]. In the assessment conducted in March 2023, as presented in Table 11 and Figure 6, the RPM-ETC model showcased significant superiority over the LSTM, RNN, and Transformer models. The RPM-ETC model achieved an accuracy rate of 86.3%, notably surpassing LSTM's 70.3% and RNN's 79.1%, indicating its superior overall performance in fault prediction tasks. Although the RPM-ETC did not exhibit an absolute advantage in terms its true negative rate (76.3%), precision (14.9%), and recall (33.1%) values, its relatively high accuracy combined with a high processing speed (386.3 frames per second), ensured its efficiency and reliability in practical applications. Additionally, the parameter count of the RPM-ETC model, at 20.18 M, while higher than LSTM (7.88 M) and RNN (5.38 M), remained relatively lower compared to Transformer (53.01 M), indicating its ability to achieve higher accuracy while maintaining a moderate level of model complexity. The RNN model, due to the problem of gradient vanishing, struggles to capture long-term dependencies when processing long sequences and can only effectively utilize short-term contextual information. Therefore, its ability to extract features from overly long sequences is insufficient. Additionally, the computational process of RNN is stepwise, requiring sequential computation for each time step, which results in low computational efficiency. The LSTM model partially addresses the gradient vanishing problem through gating mechanisms. However, when the input sequence length becomes excessively large, LSTM models tend to forget distant features, making their ability to extract long-term features similarly inadequate. MiniRocket is a fast time series classification method that transforms input time series using fixed convolutional kernels, applying Proportion of Positive Values (PPVs) pooling to extract features, and ultimately completing the classification task through a linear classifier [41]. This design significantly improves the efficiency of time series classification while maintaining high classification accuracy. However, since MiniRocket is primarily designed for univariate time series data, its ability to extend to multivariate time series is limited. Furthermore, the random selection of channel combinations may lead to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Month** & **Accuracy (\%)** & **TNR (\%)** & **PR (\%)** & **Recall (\%)** & **FPS** & **Param (M)** \\ \hline
3 & 86.3 & 76.3 & 14.9 & 33.1 & 386.3 & 20.18 \\
4 & 84.3 & 74.5 & 14.3 & 34.2 & 384.9 & 20.18 \\
5 & 86.3 & 74.6 & 14.4 & 31.5 & 388.7 & 20.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Overview of the performance of fault detection models for elevator timing information. insufficient utilization of information, and for tasks requiring deep feature representations, its performance is inferior to deep learning models. Transformer models, along with the RPM-ETC model proposed in this paper, are effective in extracting long-term sequence features as Transformer-based models. Compared to LSTM and RNN models, Transformers demonstrate superior predictive performance when handling longer input sequences.