Browsing by Author "Togni, Simone"

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    A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation
    (Elsevier, 2020-06-18) Togni, Simone; Nikolaidis, Theoklis; Sampath, Suresh
    The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics.
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    ItemOpen Access
    Comparing different schemes in a combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines online diagnostics
    (American Society of Mechanical Engineers, 2022-10-28) Togni, Simone; Nikolaidis, Theoklis; Sampath, Suresh
    The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise. The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations. The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise.
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    ItemOpen Access
    An integrated combined methodology for the outline gas turbines performance-based diagnostics and signal failure isolation.
    (Cranfield University, 2019-11) Togni, Simone; Theoklis, Nikolaidis; Sampath, Suresh
    The target of this research is the performance-based diagnostics of a gas turbine for the online automated early detection of components malfunctions with the presence of measurements malfunctions. The research proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high rate of success for single and multiple failures with the presence of measurement malfunctions – measurement noise. A combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman Filter has in his strength the measurement failure treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile, the neuro-fuzzy logic the estimation precision, used for the quantification and the fuzzy logic the categorization flexibility, which are used to classify the components failure. All contributors are also a valid technique for online diagnostics, which is a key objective of the methodology. In the area of gas turbine diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This research investigates the key contribution of each component of the methodology and reaches a success rate for the component health estimation above 92.0% and a success rate for the failure type classification above 95.1%. The results are obtained with the first configuration, running with the reference random simulation of 203 points with different level of deterioration magnitude and different combinations of failures type. If a measurement noise 5 times higher than the nominal is considered, the component health estimation drop to a minimum of 70.1% (reference scheme 1) while the classification success rate remains above 88.9% (reference scheme 1). Moreover, the speed of the data processing – minimum 0.23 s / maximum 1.7 s per every single sample – proves the suitability of this methodology for online diagnostics. The methodology is extensively tested against components failure and measurement issues. The tests are repeated with constant simulations, random simulation and a deterioration schedule that is reproducing several months of engine operations.