## 1 Introduction
These tools fall into two primary categories: (1) basic inspection devices including vibration testers, thermocouples, viscosity testers, and multimeters designed for routine measurements at predefined locations, where results are compared against established reference values, alarm thresholds, or danger levels, and (2) advanced diagnostic instruments such as spectrum analyzers, thermal cameras, microphone arrays, and real-time monitoring systems that require specialized expertise for proper data interpretation and comprehensive equipment assessment. To systematically address the complexities of industrial maintenance, the field has evolved into three distinct but interconnected disciplines: detection, diagnosis, and prognosis [16]. Fault detection involves binary classification between healthy and faulty states. It can be performed using simple tools, such as a vibration tester, followed by comparison with standards or previous records. Depending on the severity of the issue, the machine is either stopped or moved to the diagnosis phase. Fault diagnosis identifies the type of fault from among the potential issues affecting the machine. This process involves comparing fault patterns with the current behavior of the machine [17]. Once the fault is identified, the next step is either to replace the defective component immediately or proceed with prognosis. Fault prognosis monitors the health condition of the machine and estimates the remaining useful life of the equipment [18]. The purpose of subdividing this monitoring task is to allocate responsibilities among the maintenance personnel according to their expertise and provide each discipline with the necessary tools and resources. These three disciplines can be implemented using two traditional approaches: data-driven and model-based [19]. The data-driven approach relies on measurements collected through monitoring techniques to evaluate the health status of the machine, such as in -frared thermography (detects excessive heat emissions, which can indicate the presence of a fault in the machine) and vibration or acoustic measurements (utilize accelerometers or microphones and are widely employed for monitoring purposes and provide detailed insights into the machine's conditio). Other techniques may be used in specific cases or for environmental constraints. Imaging is suitable for monitoring underwater structures affected by biofouling. Oil analysis is a diagnostic tool for machines with lubrication systems, such as gearboxes and internal combustion engines. Each technique is tailored to specific applications and contributes uniquely to ensure effective monitoring and maintenance of industrial machines [11]. Sometimes the obtained data are noisy, which requires Signal Processing (SP) or image processing techniques. This approach offers numerous advantages, which explains its widespread adoption in the industry [20]. It enables real-time monitoring and can be integrated with artificial intelligence (AI) to ensure autonomy. It does not require specialized knowledge of degradation or fatigue phenomena to detect faults [21]. Additionally, it is easy to deploy, as modern machines are often equipped with various built-in sensors. By analyzing the data from these sensors, faults can be detected at their inception. However, this approach also has certain limitations; it requires a labeled dataset for all potential faults that may affect the monitored machine, a requirement that is not always feasible, especially for high-tech or recently installed machines [7]. Direct exploitation of the data is rarely possible due to acquisition noise and fault interference, necessitating efficient SP methods to extract meaningful information. This can hinder the system's ability to detect faults in real time and often prevents the physical interpretation of the underlying phenomena [21]. The model-based approach relies on the physical reproduction of the kinematic and dynamic phenomena occurring within a machine or a specific component.