# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 1 Introduction
[7] explores the simulation of thermal conductivity in bearing assemblies, emphasizing the role of thermal effects in bearing behavior. Although these studies address different aspects of fault detection and bearing performance, they collectively underscore the importance of innovative approaches in condition monitoring and predictive maintenance. Despite these advantages, independent cart systems introduce unique challenges for fault diagnosis due to their translational motion, speed variability, and modularity. Traditional fault detection techniques developed for rotary systems may not be directly applicable, necessitating novel approaches that leverage advanced predictive maintenance strategies. The latest progress in predictive maintenance and fault diagnosis has notably enhanced the surveillance of industrial equipment through the use of machine learning and deep learning techniques [8; 9; 10]. Approaches like deep convolutional networks, bidirectional LSTM (Bi-LSTM), and deep belief networks (DBNs) have been investigated for applications involving rolling element bearings and rotating machinery, attaining cutting-edge accuracy in fault detection and RUL forecasting [11; 12; 13]. Moreover, to tackle challenges in cross-domain fault classification, deep transfer learning and domain adaptation (DA) methodologies have been introduced [14; 15; 16]. Nonetheless, the use of these sophisticated methods for autonomous cart systems has not been extensively studied, with current research predominantly centered on standard rotary machinery. Additionally, there has been growing interest in cluster-based techniques for unsupervised and semi-supervised fault diagnosis, which enhance feature selection, class separability, and novelty detection [17; 18; 19]. Tactics like affinity propagation (AP), density-based spatial clustering (DBSCAN), and subspace clustering have effectively been utilized in rotating machinery for feature extraction and identification [20; 21; 22; 23]. Despite their success, these techniques must be tailored to suit independent cart systems because of their highly variable, non-synchronous movement and changes in speed. Additionally, one major obstacle in condition monitoring is the presence of imbalanced data sets, which can significantly impact the accuracy of classifiers. Methods such as Cluster-MWMOTE [24] and SMOTE with PCA [25] have been suggested to counter bias in fault classification, especially in cases where some fault conditions are more prevalent. Considering that independent cart systems may have multiple bearings per cart, data set imbalances and class representation can be decisive issues in developing reliable fault detection algorithms. To improve automated fault detection, multisensor fusion has been investigated within industrial condition monitoring. Techniques such as frequency domain multilinear principal component analysis (FDMPCA) [26] and graph-based manifold learning [27] have proven to be effective when integrating vibration, acoustic and various sensor modalities for enhanced fault diagnosis. Despite this, independent cart systems add further complexity due to interactions among multiple bearings, guide rails, and system variables. This complexity may necessitate new multisensor fusion approaches for efficient fault detection. Despite advances in fault detection methodologies, their practical implementation in independent cart systems remains challenging due to the complex motion dynamics of the system and the economic consequences of prolonged downtime. Given the substantial initial investment required for these systems, uninterrupted operation is critical for economic feasibility. However, unexpected failures can severely impact productivity and profitability. Therefore, continuous condition monitoring is essential to prevent costly breakdowns, ensure early fault detection, reduce downtime, and protect long-term operational efficiency.