Although the fouling of heat exchanger systems has been the subject of so much research in the recent past [7], there are still a lot of contributions and improvements that are needed to fully understand how to handle and manage some of the specific heat exchanger fouling problems. Among these problems is how to address reliability issues (fault diagnosis and prognosis) in the non-intrusive continuous sensors that are used in heat exchanger systems. Non-intrusive continuous sensors are mainly deployed in heat exchanger systems for real-time fouling layer detection [8]. These sensors are used as units for monitoring and controlling the heat exchanger system architecture [9], through which faults in the system and system performances are determined. Considering the very important role of these sensors in the heat exchanger systems used in power generation companies, there is a need for a comprehensive approach to the reliability and performance management of the sensors used in fault detection in the system's architecture. Moreover, a balance is required to keep false-positive rates low during the operational period of the system. False positives reduce diagnostic confidence and necessitate unnecessary and costly mitigation actions, lowering system productivity. The quality of the health management system for the sensors, therefore, can be concluded to have a direct effect on the performance of the entire system. On this basis, this study seeks to investigate and propose a model for the evaluation of the reliability and performance of the sensors used in heat exchanger systems by using a combination of methodologies. Diagnostic and prognostic analysis methods are fast-growing engineering science tools that are focused on the prediction and health condition management of engineering systems and their components based on their current and previous status. The main goal of the analysis is to provide an accurate prediction of the reliability [10], availability, performance [11; 12], and remaining useful life of the system. Based on this preposition, they are expected to efficiently monitor and track the health status of a system during operation and to determine its reliability and availability, as well as its performance and remaining useful life. Data from the processes are extracted from relevant features associated with the degradation condition and failure of the system or components [13] using sensors. In heat exchanger systems, non-intrusive continuous sensors have been suggested [8]. Recently, it has been found, however, that this sensor is affected by so many operational and environmental conditions [14]. In order to ensure the functionality of the sensor system, there is a need for a methodology and procedure for monitoring and addressing the different problems and challenges associated with the system. Any fault leading to failure in these sub-systems needs to be efficiently identified and properly isolated using the limited set of sensor signals available [15]. Given the importance of non-intrusive continuous sensors developed for real-time detection of fouling layers in heat exchanger systems, which several companies and heat exchanger plants have recently adopted, it is critical that an intelligent model is in place to monitor, evaluate, and study their reliability, availability, and performance. The development of an intelligent model is not only important for the heat exchanger system but will also help to address the several research reports that concluded that the reliability of sensors in heat exchanger systems is the main barrier to their integration into our everyday lives in the factory [16]. In this paper, an intelligent model (the K-mean clustering model) is proposed for addressing and dealing with reliability-related issues in the non-intrusive continuous sensors used for real-time fouling-layer detection in heat exchanger systems.