## 1 Introduction
These operational challenges can also damage an electricity provider's reputation, lead to contractual penalties, and compromise the reliability of the wider electricity grid. Therefore, the regular maintenance and monitoring of gas turbines is very important to reduce these challenges [13]. A variety of sensors and techniques are used to detect and prevent problems (e.g., faults) in gas turbines. One of the main methods is vibration analysis, in which piezoelectric accelerometers play a key role [14]. These sensors measure vibration abnormalities that, if left unchecked, can lead to potential imbalance or bearing failures. Complementing this inspection is thermography, where tools such as infrared thermometers and advanced thermal cameras map out temperature profiles [15]. Their ability to detect even small thermal deviations can highlight operational inefficiencies, wear, or potential component failures. Additionally, the different world of acoustic emissions is driven by the use of specialized piezoelectric sensors or high-quality microphones [16]. These devices, attuned to high-frequency sound waves, are essential for proactively detecting internal cracks or leaks. Oil analysis, critical to maintenance, uses spectrometry to identify unwanted metal contaminants. Moreover, digital viscometers and ferrography provide accurate assessments of lubricant quality, sig  
Figure 1: A damaged turbine in the Kirkuk gas power plant located in Iraq. naling potential wear and degradation [17]. Performance monitoring uses various sensors such as pressure transducers, flow meters, and thermocouples to provide instant data [18]. This helps to quickly detect any deviation from normal performance standards. In the case of Non-Destructive Testing (NDT) [19], the X-ray machine is restrained to identify defects in such a way that the integrity of the equipment is maintained. Modern control systems, supported by platforms such as SCADA and PLC, seamlessly integrate with countless sensors and provide a comprehensive diagnostic view. This technological advantage is enhanced by data analytics and machine learning. With countless sensors continuously sending data to cloud systems, advanced AI algorithms analyze this vast information and predict wear trends and possible component failures [20]. In addition, precision instruments such as digital pressure gauges and ultrasonic flowmeters increase our understanding of system health and highlight any irregularities. The proposed sensors can provide immediate insights into the operational status of a system. However, machine learning can perform a more comprehensive analysis of this collective information and identify patterns or anomalies that can be a sign of impending faults [20]. This method enables a more proactive approach to maintaining system integrity and predicting and preventing potential failures before they occur. For instance, a study titled \"On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach\" emphasizes the application of deep learning for anomaly detection in gas turbine combustors and emphasizes the accuracy of unsupervised learning models [21]. Another significant contribution is the introduction of a novel fault detection method for gas turbines using transfer learning with Convolutional Neural Networks (CNNs), as detailed in \"A novel gas turbine fault diagnosis method based on transfer learning with CNN\". This method highlights the superiority of CNNs over other Deep Neural Networks (DNNs) in gas turbine fault diagnosis [22]. Furthermore, the research on \"Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks\" addresses the importance of model interpretability and shows that partially interpretable CNNs can play a role in fault diagnosis [23]. In addition to the above, many studies have been conducted in this field, and it is not possible to include all of them in this article. However, Failure Mode and Effects Analysis (FMEA) can also be effectively used in this field [24].