K. Papailiou

Optimal Measurement and Health Index Selection for Gas Turbine Performance Status and Fault Diagnosis

In this paper, the authors present a method for defining the health estimation parameters and the measurements that must be used when a monitoring system for an engine is being set up. The particular engine layout, the available measuring instruments, and the accuracy by which data can be collected are the factors taken into account. The particular health condition estimation factors that have to be used are defined as a function of this information and the desired depth of fault identification. A fast selection procedure based on the method of singular value decomposition is presented. The uncertainty in the estimations is also derived, thus giving an additional element of information useful for decision making. The proposed method, together with adaptive performance modeling, provides a self-sufficient tool, which can be applied for setting up and subsequent exploitation of a health monitoring expert system. The advantage of the procedure is that it provides a frame of application, allowing quick implementation in a new engine of interest, other than the ones previously considered.

Turbocharger Unstable Operation Diagnosis Using Vibroacoustic Measurements

The possibility to detect unstable operating condition (stall or surge) of an automotive turbocharger using vibration or acoustic measurements is studied. An experimental study is performed, in order to acquire and analyze test data, to find out whether vibration or acoustic measurements can be correlated to aerothermodynamic operating condition. An instrumentation set allowing the definition of the operating point on the map of the compressor of the turbocharger is used. Hot wires at the compressor inlet serve as flow condition indicators and provide a clear indicator of the presence or not of instabilities, such as rotating stall or surge. Accelerometers are mounted on the casing and microphones are placed in the vicinity of the compressor casing, to measure vibration and sound emission. Data covering an extensive range of the compressor performance map have been collected and analyzed. Signal features from the different measuring instruments are discussed. Using such features, a bi-parametric criterion is established for determination of whether the compressor operates in the stable part of its performance characteristic or in the presence of unstable operation phenomena (rotating stall, surge). The possibility of generalizing the validity of observations is supported, by presenting results from testing a second turbocharger, which is shown to exhibit similar behavior.

Fast Response Wall Pressure Measurement as a Means of Gas Turbine Blade Fault Identification

Measurement of the unsteady pressure field near the wall provides information on such flow and pressure distortions and can thus be used for diagnosic purposes. An experimental investigation of the compressor rotating blade pressure field of an industrial gas turbine has been undertaken, in order to demonstrate the feasibility of the above mentioned principle. Various realistic gas turbine blade faults have been examined. Application of the appropriate processing techniques demonstrates that unsteady pressure measurements can be used to identify the occurrence of minor blade faults as well as the kind of fault

Optimizing Automated Gas Turbine Fault Detection Using Statistical Pattern Recognition

A method enabling the automated diagnosis of gas turbine compressor blade faults, based on the principles of statistical pattern recognition, is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurement data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurement data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an industrial gas turbine while extension to other aspects of fault diagnosis is discussed.

Combination of Different Unsteady Quantity Measurements for Gas Turbine Blade Fault Diagnosis

The exploitation of different unsteady quantity measurements for identifying various blade faults is examined in this paper. Measurements of sound emission, casing vibration, shaft displacement and unsteady inner wall pressure are considered. It is demonstrated that particular measurements are sensitive to specific faults. The suitability of measuring each of the above physical quantities for tracing the existence of each kind of fault is discussed. The advantage of combining different measurements originates from the possibility of extending the fault repertory covered when only one particular quantity is considered. The data analysis techniques employed range from conventional signal processing to the derivation of acoustic images of the engine outer surface. Relative features of each technique, as to their effectiveness and level of intrusivity, are discussed.Copyright

Casing Vibration and Gas Turbine Operating Conditions

The results from an experimental investigation of the compressor casing vibration of an industrial gas turbine are presented. It is demonstrated that statistical properties of acceleration signals can be linked with engine operating conditions. The power content of such signals is dominated by contributions originating from the stages of the compressor, while the contribution of the shaft excitation is secondary. The transfer functions allow reconstruction of unsteady pressure signal features from the accelerometer readings

A Procedure for Automated Gas Turbine Blade Fault Identification Based on Spectral Pattern Analysis

A method for diagnosing the existence and the kind of faults in blades of a Gas Turbine compressor is presented in the present paper. The innovative feature of this method is that it performs the diagnosis automatically, namely it gives a direct answer to whether a fault exists and what fault it is, without requiring the interpretation of results by a human expert. This is achieved by the derivation of the values of discriminants calculated from spectral patterns of fast response measurement data. A decision about the corresponding Engine status is then derived according to the values of these discriminants. In the paper, the procedure of examining the suitability of particular parameter discriminants and the constitution of a related knowledge base is described. The way of derivation of decisions by a computer, on what engine condition a particular measurement data set corresponds, is then described.Copyright

A Methodology for the Design of Automated Gas Turbine Diagnostic Systems

A methodology for the design of automated diagnostic systems for Gas Turbines is presented. The first stage of the proposed methodology consists in an initial selection of instruments and measuring positions on the engine, based on a basic knowledge of the engine itself and previous experience, as well as modelling capabilities of the phenomena happening in it. It is followed by a stage of “learning” experiments. One purpose of these experiments is to provide measurement data, on which a final selection of instruments will be based. The instruments most suitable for the fault cases of interest are selected, according to the diagnostic potential they offer. Another purpose is to develop procedures of automated fault diagnosis. The necessary background information for the later exploitation of the system is also established. The applicability of the entire methodology is demonstrated for the case of designing a blade fault diagnostic system for an Industrial Gas Turbine.© 1993 ASME

Optimal Measurements and Health Indices Selection for Gas Turbine Performance Status and Fault Diagnosis

In this paper, we present a method for defining the health estimation parameters and the measurements which must be used, when a monitoring system of an Engine has to be set-up. The particular engine layout, the available measuring instruments and the accuracy by which data can be collected are the factors taken into account. The particular health condition estimation factors, which have to be used, are defined as a function of this information and the desired depth of fault identification. A fast selection procedure based on the method of Singular Value Decomposition is presented. The uncertainty in the estimations is also derived, giving thus an additional element of information useful for decision making. The proposed method together with Adaptive Performance Modelling provides a self sufficient tool, which can be applied for setting up and subsequent exploitation of a health Monitoring Expert System. The advantage of the procedure is that it provides a frame of application, allowing quick implementation in a new engine of interest, other than the ones previously considered.Copyright