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The purpose of this paper is to develop an automated engine health monitoring system (AEHMS) for commercial aircraft.

The AEHMS is developed by using fuzzy logic. The input of the fuzzy logic is engine performance parameters gathered from aircraft for every flight during cruise. The fuzzy rule inference system for different engine faults is based on expert knowledge and real life data in the Turkish Airlines fleet. The very smallest is used for defuzzification, since it provides a more meaningful result than others. The complete loop of engine health monitoring (EHM) is automatically performed by the programs and Fuzzy Logic Toolbox in MATLAB. The system produces output values between 0 – faulty and 1 – not faulty for every fault or deterioration on a time series. The program triggers an alert if any output exceeds a specified value. Finally, the method is utilized for monitoring the engines in the Turkish Airlines fleet.

Health monitoring has been a very popular subject to increase aircraft availability with the minimum maintenance cost. Fuzzy logic is a very useful method for automated health monitoring strategies.

It does not provide long‐term engine maintenance decisions such as scheduling overhaul times, predicting the remaining life of the engine components.

The paper provides a robust method for EHM with the application to real aircraft data. The AEHMS can greatly simplify the EHM system for airlines and minimizes its drawbacks, such as extra labor hours, human error and requirement for engineering expertise.

The purpose of this paper is to model how geometric errors of a machined surface (or manufacturing errors) are related to locators’ error, workpiece form error and machine tool volumetric error. A kinematic model is presented that puts into relationship the locator error, the workpiece form deviations and the machine tool volumetric error.

The paper presents a general and systematic approach for geometric error modelling in drilling because of the geometric errors of locators positioning, of workpiece datum surface and of machine tool. The model can be implemented in four steps: (1) calculation of the deviation in the workpiece reference frame because of deviations of locator positions; (2) evaluation of the deviation in the workpiece reference frame owing to form deviations in the datum surfaces of the workpiece; (3) formulation of the volumetric error of the machine tool; and (4) combination of those three models.

The advantage of this approach lies in that it enables the source errors affecting the drilling accuracy to be explicitly separated, thereby providing designers and/or field engineers with an informative guideline for accuracy improvement through suitable measures, i.e. component tolerancing in design, machining and so on. Two typical drilling operations are taken as examples to illustrate the generality and effectiveness of this approach.

Some source errors, such as the dynamic behaviour of the machine tool, are not taken into consideration, which will be modelled in practical applications.

The proposed kinematic model may be set by means of experimental tests, concerning the industrial specific application, to identify the values of the model parameters, such as standard deviation of the machine tool axes positioning and rotational errors. Then, it may be easily used to foresee the location deviation of a single or a pattern of holes.

The approaches present in the literature aim to model only one or at most two sources of machining error, such as fixturing, machine tool or workpiece datum. This paper goes beyond the state of the art because it considers the locator errors together with the form deviation on the datum surface into contact with the locators and, then, the volumetric error of the machine tool.