## 1 Introduction
These machines are valuable testbeds to study predictive maintenance techniques and are hereby utilized to develop a robust machine learning-based classifier that leverages multi-sensor data fusion for fault detection and classification. One of the challenges of IFD is the lack of a real-world dataset collected under industrial working conditions. Most studies are based on datasets generated under laboratory conditions [31; 32; 33; 34; 35]. However, such datasets often exclude or lack factors such as environmental and operational variables. In fact, these factors can significantly affect machine performance indicators and therefore need to be considered [29] for a robust condition monitoring regime. Datasets that reflect real-world conditions are critical for developing more reliable and generalizable models sensitive to environmental and operational changes. In this context, one of the highlights of this study is that it provides a new dataset collected under real-world conditions. The machine subject analyzed here had a known developing issue of an imbalance in the rotor--which was also verified by examining the data collected via industrial portable analyzers producing a dominant frequency component in the velocity spectrum at exactly the rotation frequency in both the horizontal and vertical directions--generating an extra load due to centrifugal force on the bearings [2]. Such a mechanical fault resulted in an excessive level of velocity amplitudes (mm/s) in the horizontal and vertical axes due to the mismatch of the center of mass and the axis of rotation and a high level of ultrasound due to the increased level of angular contact affecting a relatively smaller diameter (load zone) in the bearing races [36]. As such, this dataset will contribute to research in the field of IFD, allowing a better understanding of the effects of environmental and operational variables. This study focuses on the following topics:  
* Introducing a novel framework for integrating and analyzing multi-modal sensor data for fault diagnosis. * Benchmarking machine learning classifiers to identify the most effective methods for fault classification. * Providing insight into the practical implementation of such methodologies in industrial applications. * Studying possible performance issues related to the quality of raw data, indexes derived from raw data, and identifying the need for additional parameters, both in the time and frequency domains. * Another, and perhaps the most important, contribution of this study is that it allows for the development of more reliable and generalizable models using a new dataset collected under real-world conditions. The structure of this article is organized as follows: In Section 2, previous studies are presented in a review of the literature. In Section 3, the theoretical background and machine learning algorithms used in this study are discussed in detail. Comprehensive information on data collection methods, preparation stages, dataset characteristics, and the experimental environment is presented in Section 3.2. In Section 4, the analysis results are discussed, and the performance of the models is compared and analyzed. Finally, in Section 5, the findings and conclusions are summarized, and in Section 6, the study is concluded by including limitations and suggestions for future research.