## 3 Results and Discussion
### Up Movement  
We have analyzed up and down movements separately because the traction-based elevator usually produces slightly different levels of vibration in each direction. First, we have selected faulty rides based on time periods provided by the maintenance data, including all floor patterns, which is fed to the algorithm for profile extraction, as shown in Figure 4. Then, we have selected an equal number of rides for healthy data, and the extracted profiles are shown in Figure 5. Visualization of the profiles proved that our proposed algorithm extracted elevator start and stop events have equal length irrespective of the floor combination as shown in Figures 4 and 5.  
The next step is to label both the healthy and faulty profiles with class labels 0 and 1, respectively. Healthy and faulty profiles with class labels are fed to the deep autoencoder model and the generated deep features are shown in Figure 6. These are called deep features or latent features in deep autoencoder terminology, which shows hidden representations of the data. In Figure 6, we can see from visualization that both features with class labels are perfectly separated, which results in better fault detection.  
Extracted deep features are fed to the random forest algorithm for classification, and the results provide 90% accuracy in fault detection as shown in Table 3. We have compared accuracy in terms  
Figure 4: Profiles from faulty rides (The figure shows first 10 rides from the combined version of elevator start and stop events from magnetic signals in z-direction i.e., Mag vertical. Profiles are extracted from all floor combinations during the time period of fault occurred and fixed by maintenance personal in upward movement of elevator system. Color labelling is based on maximum number of rides available in the data during this time period i.e., variable.).  
of avoiding false positives from both features and found that new deep features generated in this research outperform the existing features. We have used the remaining healthy rides for extracting profiles to analyze the number of false positives. These healthy profiles are labelled as class 0 and fed to the deep autoencoder to extract new deep features from the profiles, as shown in Figure 7.  
These new deep features are then classified with the pre-trained deep autoencoder random forest model to test the efficacy of the model in terms of false positives. Table 3 presents the results for upward movement of the elevator in terms of accuracy, sensitivity, and specificity. We have also included the accuracy of avoiding false positives as an evaluation parameter for this research. The results show that the new deep features provide better accuracy in terms of fault detection and avoiding false positives from the data, which is helpful in detecting false alarms for elevator predictive maintenance strategies. It is extremely helpful in reducing unnecessary visits by maintenance personnel to installation sites.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Deep Features** & **Existing Features** \\ \hline
**Accuracy** & 0.90 & 0.54 \\
**Sensitivity** & 0.92 & 0.50 \\
**Specificity** & 0.88 & 0.58 \\
**False positives** & 0.86 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault detection analysis (False positives field related to analyzing remaining healthy profiles after the training and testing phase).  
Figure 5: Profiles from healthy rides (The figure shows first 10 rides from the combined version of elevator start and stop events from magnetic signals in z-direction i.e., Mag vertical. Equal number of profiles as in Figure 4 are extracted from all floor combinations before the time period of fault occurred in upward movement of elevator system. Color labelling is based on number of rides available in the data during this time period i.e., variable.).  
Figure 6: Extracted deep autoencoder features (visualization of the features w.r.t class variable).  
Figure 7: Extracted deep features (only healthy profiles).