# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 1 Introduction
Although independent cart systems are increasingly replacing traditional conveyor belt systems, research on condition monitoring for such systems remains limited. This gap is largely due to confidentiality concerns, often enforced by non-disclosure agreements, which restrict the sharing of vibration, acoustic, or other relevant data. Consequently,there are no public repositories or databases for conducting condition monitoring studies on independent cart systems. To the best of the authors' knowledge, there is almost no academic research available in this domain. This study addresses this critical gap by providing a comprehensive analysis and a publicly accessible data set that serves as a foundational resource for future research in this emerging field. The unique contribution of this paper lies in its focus on independent cart systems, a topic that has been largely overlooked, and its provision of valuable data and insights that were previously unavailable. These factors underscore the significance and novelty of this work, making it a strong candidate for publication. Furthermore, the inherent challenges associated with the condition monitoring of independent cart systems further underscore the need for this research. These systems are highly non-synchronous, with speed variations ranging from a few millimeters per second to several meters per second. For example, the system under study can achieve cart speeds of up to 4 m per second. Moreover, independent cart systems allow for speed reversal, enabling changes in direction. The complexity is further compounded by the modular nature of these systems, where each cart contains multiple bearings. For example, the system under study includes three bearings per cart, and with the addition of each cart, the total number of bearings increases by three. In practical industrial applications, where hundreds of carts may be in operation, this translates into monitoring the condition of hundreds of bearings. This makes fault identification and location significantly more challenging. Another significant motivation for this research lies in the fundamental differences between independent cart systems and conventional rotary machinery, which form the basis for most publicly available rolling element bearing data sets. In conventional systems, the bearings are fixed around a rotating shaft, and most data sets, such as those referenced in [28; 29; 30; 31], are designed for fault detection in these stationary configurations. Throughout the years, numerous publicly accessible data sets have been developed to assist in diagnosing faults in rotary systems. Notable examples include the XJTU-SY data set for rolling bearings [32], the SEU gearbox data set aimed at detecting gear faults [33], and the Airbus PHM challenge data set [34], which is dedicated to prognostics and health management. These data sets offer invaluable tools for assessing the condition of conventional machinery. Nonetheless, they fail to tackle the specific challenges presented by independent cart systems, where the movement involves both rotational and translational dynamics along a track. Unlike rotary machinery, where fault progression is primarily influenced by rotational dynamics, independent cart systems introduce a complex interaction between translational motion, system variability, and fault propagation. This fundamental difference makes independent cart systems depart significantly from standard data sets and methodologies, emphasizing the need for specialized research in this area. The data set presented in this study offers significant benefits in advancing research on condition monitoring and predictive maintenance for independent cart systems. It provides a comprehensive and publicly accessible resource that captures the unique dynamics of these systems, including vibration signatures, internal system variables, and fault characteristics in various speed profiles.