# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 9 Conclusions  
This study presents a comprehensive data set and analysis framework for the monitoring of the condition of independent cart systems, addressing the unique challenges posed by their complex configurations and motion dynamics. A key aspect of these systems is their scalability, where the addition of each cart increases the number of bearings by three, significantly amplifying the complexity of fault detection and localization. Furthermore, the unique translational and rotational dynamics of bearings, as they glide along the track, introduce challenges that differ fundamentally from the conventional rotary machinery. Through a systematic experimental campaign, data sets were collected under varying fault types, severities, and configurations, with a focus on single-cart and three-cart setups. The analysis employed classical statistical parameters, including mean, skewness, kurtosis, crest factor, and similar metrics, ensuring a straightforward yet effective approach for fault identification. Dimensionality reduction techniques such as PCA and t-SNE were also used to analyze the high-dimensional feature set, revealing critical insights into fault separability across different classification methods and experimental conditions. The results demonstrate the ability of PCA to distinguish between normal and faulty states, particularly for simpler configurations and smaller fault sizes. However, for complex scenarios, such as the three-cart experiments with overlapping fault distributions, t-SNE provided superior separability and visualization, especially when combined with robust distance metrics. These findings highlight the value of combining classical statistical features with modern dimensionality reduction techniques to provide simple but effective solutions for fault identification in industrial settings. The study also highlights the limitations of existing techniques in achieving compact clustering and fault localization, particularly for overlapping fault distributions and unsupervised ranking methods like the Laplacian score. Future work will focus on developing advanced clustering and transformation techniques to address these challenges, with a particular emphasis on resolving the issue of overlapping fault distributions. This includes enhancing fault discrimination between closely spaced  
Figure 16: t-SNE representation of the ranked feature set from Experiment Type 8, illustrating the IR and OR faults of the top bearing, with the IR fault oriented at 3 O’Clock. The features are computed for each complete cycle of the M1 mover. fault conditions by leveraging adaptive feature transformations, improved metric learning, and advanced decision boundary refinement methods. These developments aim to improve the interpretability of fault signatures and ensure more reliable fault classification, even in complex multi-cart configurations. The potential for failure prediction under broader experimental conditions is an important direction for future research. While the current study primarily focused on speed profiles of 1000 mm/s and 2000 mm/s, the developed methodology can be extended to a wider range of operating conditions. Future work could explore varying load conditions, acceleration profiles, and adaptive speed scenarios, which are more representative of real-world industrial settings. Additionally, incorporating environmental factors such as temperature variations, lubrication conditions, and external disturbances could further enhance the generalizability of the proposed approach. Furthermore, the inclusion of detailed experimental data in an open-access repository ensures that this work serves as a valuable resource for the research community, fostering innovation in predictive maintenance and machine health monitoring for modern industrial transport systems. By making the data set openly accessible, this study provides a foundation for further research in predictive maintenance and condition monitoring, not only for independent cart systems but also for other industrial applications.