features against known patterns of faults. This is commonly performed either by statistical methods involving the analysis of the statistical properties of the vibration signal features. Control charts and statistical hypothesis testing are commonly applied in this regard to detect deviations from normal behavior [12]. The alternative involves advanced machine learning algorithms, such as support vector machines, neural networks, and decision trees, which can be trained on historical vibration data to recognize patterns associated with specific faults, enabling automated and accurate fault identification [13, 14]. The integration of vibration monitoring with signal processing and pattern analysis offers several benefits:  
* Early Fault Detection: Allows for the identification of faults at an early stage, preventing catastrophic failures and reducing maintenance costs. * Improved Reliability: Enhances the reliability and efficiency of machinery by enabling condition-based maintenance rather than time-based schedules. * Data-Driven Decisions: Provides actionable insights based on real-time data, facilitating informed maintenance decisions. However, challenges remain in the implementation of these techniques. The complexity of rotating machinery and the variability of operating conditions can complicate the analysis. Furthermore, the initial setup of monitoring systems and the development of accurate diagnostic models require significant expertise and resources. Also, a gap exists between research advancements and industrial adoption in diagnostic models. Researchers often focus on cutting-edge technologies that are in early development stages, while industries prefer mature, proven solutions. High implementation costs, complexity, and specialized knowledge requirements can deter companies from adopting new research-based methodologies. Additionally, the extensive data requirements for advanced diagnostics pose a challenge for industries with limited data collection capabilities. While academia often pursues novel discoveries, industrial interests are driven by practical benefits like cost reduction and efficiency improvement. Customization and specificity of research solutions may not directly translate to the diverse environments of industrial applications, necessitating adaptable technologies. It is then the aim of this Special Issue to provide some novel tools to tackle the diagnostic of different rotating machinery from several different areas, driven by industrial needs. In particular, it was decided to prefer works on available benchmark datasets coming from both research laboratories and real industrial applications. For example, the NASA Prognostics Data Repository [15] collects a wide variety of datasets that are essential for the development and validation of prognostic algorithms and methodologies. These datasets include real-world and simulated data on system performance, health, and failures, and are widely used for research in predictive maintenance and fault detection. Gearboxes, bearings electric motors, pumps and structural components data are available. Additionally, universities such as the Case Western Reserve University [16] and the Politecnico di Torino [17] shared rolling elements bearings datasets from laboratory test rigs. Also, partial datasets from International Conferences such as Surveillance 8 and CMMNO 2014 are freely available online, featuring the Instantaneous Angular Speed (IAS) estimation of a SAFRAN aircraft engine and a wind turbine gearbox [18, 19, 20], while large repositories called Data Lakes are aggregated from the U.S. Department of Energy's Programs, Offices, and National Laboratories under the name of Open Energy Data Initiative (OEDI) and store information from a variety of sources: private industry, laboratories, analytic tools, use cases, research reports, and more [21].