## 1 Introduction  
Industrial rotating machinery is the engine that powers economies through continuous production. Therefore, they must be reliable and efficient to operate without unexpected failures, especially in manufacturing industries. Their failure would lead to costly, unscheduled downtime as well as safety and productivity losses [1, 2]. For these reasons, effective maintenance strategies are essential. Maintenance approaches are categorized into three parts: reactive, preventive, and predictive maintenance [2, 3, 4]. Among these, predictive maintenance (PdM) has gained popularity due to its ability to predict and prevent failures before they occur. Real-time sensors and monitoring systems can assess the rotating machinery's health and minimize unplanned downtime, production loss, and associated costs. It is also possible to apply advanced technologies, such as IoT devices and machine learning-based early warning systems, to deliver practical monitoring insights. As a result, PdM has become an integral part of modern industrial systems. PdM utilizes several diagnostic techniques, among which vibration analysis, temperature monitoring, and ultrasound are the most widely used methods [5; 6; 7; 8; 9]. Vibration monitoring is used to identify mechanical faults such as misalignment, imbalance, and gear and bearing defects [1; 10; 11]. Temperature monitoring is used to observe whether there are overheating issues and/or inefficient lubrication [12; 13; 14]. Ultrasound can detect early-stage wear and structural defects in bearings and gears that would otherwise be detected later by vibration analysis [5]. Although each parameter provides insight into the working conditions of the rotating parts, integrating them into multi-modal data is still a significant challenge, especially when dealing with complex datasets [15; 16]. Fault diagnosis through ultrasound and vibration analysis helps to understand the health status of industrial machines [1; 2]. Historically, the relationship between data monitoring and the health status of machines has been observed by engineers' fault diagnosis skills based on field experience. For example, motor faults can be diagnosed through sounds, while vibration signals collected by portable devices have been manually analyzed to locate bearing faults [17]. However, automating this process would not only shorten the maintenance cycle but could also increase diagnostic accuracy. In this context, a special term has been attributed to the application of machine learning methods in machine fault diagnosis: intelligent fault diagnosis (IFD) [18; 19]. This approach proposes machine learning algorithms to train diagnostic tools using the data collected from machines, rather than providing a diagnosis based on the knowledge of maintenance engineers. Applying machine learning to improve fault classification for the detection and diagnosis of machine faults has been the focus of academic research since the 1990s, and studies in this area have gained momentum since 2015 [17]. Intelligent fault diagnosis expands the capabilities of engineers limited by traditional methods, providing a faster, more accurate, and data-driven diagnostic process. This approach reduces human-induced errors in fault detection and enhances the efficiency of industrial machinery. Processing data from multiple sensors enables machine learning models to provide conclusive evidence for known failures and generate more accurate predictions. However, integrating multi-sensor data poses unique challenges [20; 21]. Fault diagnosis is typically performed on machine components designed to reduce friction and ease rotational movement, such as bearings [9; 22], stators [23], gears [24; 25], rotors [26], shafts [27; 28], and others [29]. This study is motivated by the need to fill the gaps in the existing literature, particularly the limited exploration of multi-modal data integration for fault classification in rotating machinery [30].