Multipoint Non-Linear Method for Enhanced Component and Sensor Malfunction Diagnosis

Authors:Kamboukos Ph., Mathioudakis K.

Abstract

Operating gas turbine engines are usually equipped with a limited number of sensors. This situation is the common issue of gas turbine diagnostics where the absence of sufficient measurements from the engine gas path reduces the effectiveness of the applied methods. In addition the installed sensors of the engine deteriorate with time or present abrupt malfunctions which are not always detectable. One way to overcome this problem is the exploitation of information from a number of different operating points by constructing a multipoint diagnostic procedure. Information from different operating points is combined in order to increase the number of measurements and thus to form a well determined diagnostic system for the estimation of engine component health parameters. The paper presents the extension of the method in order to be able to assess both engine and sensors state. Initially the ability of the method to estimate the condition of a high bypass turbofan engine, exploiting information from different instances of its flight envelop is depicted. The problem of selecting the appropriate operating points is analyzed on the basis of the numerical condition of the formed diagnostic system. The method is also applied to a single shaft turbojet, for estimation of engine component health parameters and sensors state. Finally a number of aspects related to the formulation of the method are examined. These are the comparison between full method and its linear approximation, the effect of measurement noise on the derived estimation and the computational cost.

Identification of Sensor Faults on Turbofan Engines Using Pattern Recognition Techniques

Authors:Aretakis N., Mathioudakis K., Stamatis A.

Abstract

The possibility to identify faults in the readings of the sensors used to monitor the performance of a high by-pass ratio turbofan engine is examined. A novel method is proposed, based on the following principle: if a measurement set is fed to an adaptive performance analysis algorithm, a set of component performance modification factors (fault parameters) is produced. Faults, which may be present in the measurement set, may be recognized from the patterns they produce on the modification factors. The constitution of a method of this type based on pattern recognition techniques is discussed here. Test cases corresponding to different sensor faults, simulating operation in real conditions are examined. Three kinds of pattern recognition techniques with increasing complexity are used, in order to correctly identify the examined sensor faults. It is demonstrated that by choosing an appropriate formulation it is possible to have a 100% success in the identification of the examined sensor faults.

Probabilistic Neural Networks For Validation of On-Board Jet Engine Data

Authors:Mathioudakis K., Romessis C.

Abstract

A method is presented for identification of faults in the readings of sensors used to monitor the performance and the condition of jet engines. Probabilistic neural networks are used to detect the presence and identify the location and magnitude of faults (biases) in sensor readings. The faults can be detected on sets comprising a limited number of instruments, typical of those available for on-board monitoring of jet engines. An engine performance model is used to support the constitution of a network. Training information is built using the model to produce data for a comprehensive set of healthy and faulty situations. The network performance in detecting and quantifying sensor faults is validated on a large number of fault cases, also generated by a model, which are used for testing the network and cover a wide range of conditions that can be encountered in practice. An engine, representative of current large civil engine designs (large bypass, partially mixed turbofan), serves as the test vehicle for demonstration of the way the method is materialized.

Setting Up Of a Probabilistic Neural Network for Sensor Fault Detection Including Operation with Component Faults

Authors:Romessis C., Mathioudakis K.

Abstract

The diagnostic ability of Probabilistic Neural Networks (PNN) for detecting sensor faults on gas turbines is examined. The structure and the features of a PNN, for sensor fault detection, are presented. It is shown that with the proposed formulation, a powerful tool for sensor fault identification is produced. A particular feature of the PNN produced is the ability to detect sensor faults even in the presence of engine component malfunction, as well as on deteriorated engines. In such situations, the size of bias that can be identified increases. The way to establish the limits of sensor bias that can be detected is presented along with results from application to test cases with realistic noise magnitudes. The diagnostic procedure proposed here is also supported by an engine performance model. The data used for setting up and testing the PNN are generated by such a model.

Jet Engine Sensor Validation with Probabilistic Neural Networks

Authors:Romessis C., Mathioudakis K.

Abstract

A method of identifying faults on the sensors used to monitor the performance and the condition of jet engines is presented. The method uses probabilistic neural networks and can identify the presence of a fault in the reading of a sensor and quantify the magnitude of this fault, namely the bias from the true reading. The possibility to detect this magnitude is a novel aspect of the paper, as compared to previous work of the authors as well as the methods that exist in the open literature. The method uses an engine performance model to support the construction of the network. A sensor fault data base is formed in order to train the network. The capability of the network to correctly detect and quantify sensor faults is tested on a large number of fault cases, which are used for testing the network and covers a large area of conditions that can be encountered in practice. The effectiveness of the method is shown through its implementation in an engine which is representative of current large civil engine designs (large bypass, partially mixed turbofan).