A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5？MW gas turbine working in a natural gas compression plant is presented. 1. Introduction Maintaining high levels of availability and reliability is an essential objective for all production units, especially for those that are subject to high costs due to loss of production. Nonscheduled stops due to unforeseen faults cause relevant costs related to the reduction or the interruption of the process and to the consequent repairing actions. For this reason, in strategic applications, stand-by machines are usually required to ensure the desired level of availability. In the last decades, gas turbines have been more and more used either for power generation or as mechanical drive (e.g., in natural gas compression plants), thanks to their favorable characteristics with respect to other technologies, such as low emissions and high availability and reliability. In particular, the latter issues represent winning features of gas-turbine-based power plants. Hence, in order to utilize these systems as effectively as possible, the management of machine maintenance must be optimized. The optimization of maintenance management, which should lead to cost saving and increase in machine availability, can be performed by supporting gas turbine preventive maintenance (which comes from manufacturer experience in terms of component life and performance degradation versus working hours and is performed according to a priori schedules, regardless of the effective gas turbine health state) with on-condition maintenance, which consists of ？“ad hoc” actions descending from gas turbine actual operating state [1–7]. Therefore, On-condition maintenance requires up-to-date knowledge of the machine health state in real time. One of the most
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