## 1 Introduction  
Diagnostic methods using aero-thermodynamic data, known as gas path analysis methods, use data obtained from steady-state operation to identify the condition of a gas turbine. Numerous studies obtaining diagnoses using transient operation data have been presented; some very recent ones, for example, are presented in [1; 2; 3; 4; 5], with reviews of the existing literature extensively covered in [4; 5]. The capacity and robustness of these methods are limited, and their applicability is demonstrated on simulated data. It is noted that discrepancies in the findings of steady-state and transient diagnostics are observed in [5], while the final conclusion of the paper states that \"the combined effect of physical faults in a transient state remains an unexplored problem\". When a robust diagnostic approach is needed, the data that are to be fed to a gas path diagnostic procedure need to have been obtained from steady-state operation. This is a well-recognized fact, as can be concluded from publications originating from the personnel of engine manufacturers [6; 7; 8]. A question that arises then is: how is it ensured that this requirement is fulfilled at the operating point the data come from? The importance of identifying the steady-state operation of engineering systems in general has been recognized, and efforts towards developing algorithms that are able to identify such operation have been presented for many years (for example, [9]) using statistical concepts. The features of the problem of determining whether an operating condition can be characterized as steady-state, for the particular case of gas turbines, is also well known, and methods for its solution have been presented in the literature until very recently [10; 11; 12; 13; 14; 15; 16; 17; 18]. The motivation of exploiting test data for the purpose of engine condition monitoring was the driver of earlier methods [10; 12]. Optimizing engine test cell trials was another motive [11], as the ability to characterize the operation reduces the testing time (and thus cost) while leading to better-quality data. Although the operational features of jet engines make them more relevant to the problem at hand, it is also relevant to ground gas turbines [15]. The fact that steady-state operation constitutes the basis for reliable diagnosis using flight data has led the efforts aiming to characterize states as \"quasi steady-state\", as reported in [6; 7]. The early method introduced in [10] was used as the basis for the very recent (2022) work of [8]. Excellent reviews on the usefulness of gas turbine steady-state detection and methods, developed until recently, are presented in [8; 16]. All methods presented in the literature are based on the collection of data, which are processed in a variety of ways to determine whether the operating conditions are steady state or not. The criteria used also rely on the data themselves. In [16], for example, measurement data from operation are statistically processed in order to derive the relevant criteria. In [18], test data are used to identify clusters that are indicative of the mode of operation, with steady state being one of the modes of interest. Although the underlying physical principles are understood regarding the reasons why steady-state diagnostic methods using data from transient operation do not produce accurate information on the condition of the components, no quantification of such effects has been presented; neither has a procedure of a general nature been presented for tackling this problem in gas turbines. The focus of the method introduced in the present paper is on applications to gas turbine performance monitoring and fault diagnostics. The purpose is to analyze and quantify how steady-state diagnostic methods produce information that is not representative of the actual engine condition when fed with data from non-steady-state operating conditions.