A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Gas Path Performance Diagnostics <s> The performance-seeking-control algorithm (PSC) is designed to continuously optimize the performance of propulsion systems. The PSC uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters (EDPs) characterizing engine deviations with respect to nominal conditions. In practice, the measurement biases (or model uncertainties) may prevent the estimated EDPs from reflecting the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. An observability analysis shows that the biases cannot be estimated together with the EDPs. Moreover, biases and EDPs turn out to have equivalent effects on the measurements, leaving it undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. In this article, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine. <s> BIB001 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Gas Path Performance Diagnostics <s> Abstract Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960s, when L. A. Urban introduced gas path analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aerospace and industrial applications. To date, a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance-analysis-based methods available thus far for gas turbine fault diagnosis in the open literature. <s> BIB002
Gas path performance diagnostics involves estimating the values of specific variables associated with the gas path components, or changes in these values that might indicate a fault. It is important to distinguish between the general health of the engine gas path components and sudden faults. The general health of the engine is equivalent to its level of degradation or effective age, and is the baseline from which changes are measured. In the gas path, the health condition of each component is defined by its efficiency and other parameters that change slowly. In general there are not enough engine sensors available to allow estimation of these health parameters in flight. Some diagnostic schemes relate faults to abrupt changes in the parameters associated with component health. Thus, on-line monitoring of unmeasurable variables is the basis for many fault detection and isolation approaches. BIB002 The information flow diagram in figure 9 indicates how damage and wear is related to degraded performance.  Many linear and nonlinear techniques have been applied to this problem, but without the addition of diagnostic sensors, the problem will remain. The estimation problem arises because the number of health parameters exceeds the number of measurements, which means that the problem is underdetermined and thus the health parameter shifts can not be uniquely determined. BIB001 The problem is further complicated by estimation errors due to model mismatch, noise and sensor bias. The sensor validation issue is addressed in the section on Control System Integrity Assurance. 