Abstract: For monitoring (unmeasured) process states of a rotating machine having a combustion chamber (e.g. a gas turbine), compositions of educts entering the combustion chamber are measured. Based on the compositions of the educts, the composition of the product produced by the combustion chamber can be determined. Moreover, the mechanical power (Pmech) generated by the rotating machine can be determined. Based on the mechanical power (Pmech), the composition of the educts and product, and stoichiometric relationships of educts and product, the values of process states, such as the air mass flow (wa) through the compressor leading into the combustion chamber and/or the gas mass flow (wg), the composition and/or the temperature (T3) of exhaust gas exiting the combustion chamber can be determined. Based on precise measurements of the educt (e.g. the composition of air and fuel in the combustion process), the product (i.e.

Abstract: The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k?1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters.

Abstract: The present disclosure is concerned with the identification of fault origins of slowly degrading systems such as industrial gas turbines. Following the initial input of some parameter values, exemplary procedure comprises a number of steps which are repeated periodically during the operation of the gas turbine. First, for each potential fault such as e.g. a contamination of compressor blades, erosion of turbine blades or corrosion of machine parts, a pre-symptom fault probability, characteristic of the specific gas turbine considered, and a symptom-conditional fault probability are calculated. In a second step, on-line data from the monitoring devices are loaded in order to update information about symptoms such as e.g. reductions in polytropic efficiency and in flow capacity, changes in vibration spectrum, or other machine condition indicators, and a post-symptom fault probability is calculated.

Abstract: The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k?1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters.

Abstract: The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k?1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters.

Abstract: For monitoring (unmeasured) process states of a rotating machine having a combustion chamber (e.g. a gas turbine), compositions of educts entering the combustion chamber are measured. Based on the compositions of the educts, the composition of the product produced by the combustion chamber can be determined. Moreover, the mechanical power (Pmech) generated by the rotating machine can be determined. Based on the mechanical power (Pmech), the composition of the educts and product, and stoichiometric relationships of educts and product, the values of process states, such as the air mass flow (wa) through the compressor leading into the combustion chamber and/or the gas mass flow (wg), the composition and/or the temperature (T3) of exhaust gas exiting the combustion chamber can be determined. Based on precise measurements of the educt (e.g. the composition of air and fuel in the combustion process), the product (i.e.

Abstract: The present disclosure is concerned with the identification of fault origins of slowly degrading systems such as industrial gas turbines. Following the initial input of some parameter values, exemplary procedure comprises a number of steps which are repeated periodically during the operation of the gas turbine. First, for each potential fault such as e.g. a contamination of compressor blades, erosion of turbine blades or corrosion of machine parts, a pre-symptom fault probability, characteristic of the specific gas turbine considered, and a symptom-conditional fault probability are calculated. In a second step, on-line data from the monitoring devices are loaded in order to update information about symptoms such as e.g. reductions in polytropic efficiency and in flow capacity, changes in vibration spectrum, or other machine condition indicators, and a post-symptom fault probability is calculated.

Abstract: A rescheduling problem can be reformulated as a multi-parametric (mp-QP) optimization problem which can be solved explicitly. The subsequent exploitation of this algebraic solution is computationally inexpensive.

Abstract: The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k?1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters.

Abstract: A method for estimating a state of a system including determining, during a current cycle of the system, an estimated state of the system based on currently measured values of the system, boundary conditions of the system, an accuracy of the currently measured values, and an estimated state from a preceding cycle. The method also determines an accuracy of the estimated state, and determines an uncertainty of the estimated state based on the estimated state and the accuracy of the estimated state.

Abstract: Method and system (1) for automatically determining an overall risk resulting from a plurality of independent risk factors, in which method and system a fast Fourier transform (FFT) is calculated with the aid of an FFT processor module (4) in each case for an accepted probability density function of an independent risk factor, in which method and system the transformed probability density functions of the independent risk factors are multiplied by one another by means of a multiplication module (5), and in which method and system an IFFT processor module (6) is used to calculate a probability density function proportional to the probability density function of the overall risk by calculating an inverse FFT transform for the product calculated by the multiplication module (5).