Abstract: A measurement assembly for determining an electrical resistance of a fluid in a wellbore is provided. The measurement assembly can include a non-conductive frame, comprising an excitation electrode and a monitoring electrode coupled to the non-conductive frame. The measurement assembly can also include a central electrode in a flow path for the fluid, wherein the flow path is defined by the non-conductive frame. The central electrode can be positioned for conductively coupling the excitation electrode and the monitoring electrode through a conductive path defined by the fluid. Further, the measurement assembly can include a power source for transmitting power to the excitation electrode for determining the electrical resistance of the fluid. The electrical resistance of the fluid can be used to determine whether the fluid includes oil, water, or gas.

Abstract: A method and system for online power management of a turbine engine is provided. The method includes operating an engine control system on a first bandwidth, filtering at least one data input from the engine control system to a second bandwidth, and receiving, by a power management system operating on the second bandwidth, the at least one filtered data input. The method also includes predicting an engine operating condition using the at least one filtered data input using a closed-loop engine model, determining an optimal engine power management based on the prediction, solving a constrained optimization for a desired optimization objective, and outputting the optimal engine power management to the engine control system.

Abstract: A method and system for online power management of a turbine engine is provided. The method includes operating an engine control system on a first bandwidth, filtering at least one data input from the engine control system to a second bandwidth, and receiving, by a power management system operating on the second bandwidth, the at least one filtered data input. The method also includes predicting an engine operating condition using the at least one filtered data input using a closed-loop engine model, determining an optimal engine power management based on the prediction, solving a constrained optimization for a desired optimization objective, and outputting the optimal engine power management to the engine control system.

Abstract: A system for selecting a representative sensor set of a power plant is provided including a computer. The computer has a memory. The memory stores a list of all feasible sensor sets, and at least one variable that is used to calculate a local minimum value of a cost function and a cost function value. The computer includes control logic for executing an algorithm. The computer includes control logic for selecting a proposed sensor set from the list of all feasible sensor sets. The proposed sensor set is based on a specific iteration of the algorithm. The control module includes control logic for determining if the system is observable using the proposed sensor set. The control module includes control logic for determining if the cost function value is less than the local minimum value of the cost function, if the system is observable using the proposed sensor set.

Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method for advanced condition monitoring of an asset system includes monitoring a variable of an asset system using the at least one sensor of a smart sensor system; determining whether the asset system has departed from normal operation; and identifying the variable of the asset system indicating the departure from normal operation. In another method, the time sequential values of the monitored variable is analyzed by using a Rank Permutation Transformation test, a Hotelling's T2 statistic test, and a Likelihood Ratio Test; and a change of an operating condition of the asset system is determined using the analyzed values. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method of generating linear models for a physical system of interest is implemented in steps including, first, determining offline, a set of linear models for the physical system of interest by linearization of a nonlinear computational model of the physical system of interest at selected operating points or from desired data; second, analyzing offline, accuracy of each linear model and eliminating inaccurate linear models therefrom to provide a residual set of linear models; third, generating offline, linear models corresponding to grid points of one or more lookup tables based on the residual set of linear models; fourth, associating offline, lookup table grid points with selected scheduling variables, and fifth, generating algorithmic software for the physical system therefrom such that linear models for the physical system generated offline form the basis for online scheduling of linear models.

Abstract: A method for advanced condition monitoring of an asset system includes using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals by using a fuzzy supervisory model blender; performing a fault diagnostic on the combined residuals; and determining a change of the operation of the asset system by analysis of the combined residuals. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method for advanced condition monitoring of an asset system includes using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals by using a fuzzy supervisory model blender; performing a fault diagnostic on the combined residuals; and determining a change of the operation of the asset system by analysis of the combined residuals. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A system, a method, and an article of manufacture for adjusting CO emission levels in predetermined locations in a boiler system are provided. The boiler system has a plurality of burners and a plurality of CO sensors disposed therein. The system determines locations within the boiler system that have relatively high CO levels utilizing the plurality of CO sensors and then adjusts A/F ratios of burners affecting those locations to decrease the CO levels at the locations.

Abstract: In accordance with one exemplary embodiment the invention provides a multi-parameter fiber optic sensing system with an aperiodic sapphire fiber grating as sensing element for simultaneous temperature, strain, NOx, CO, O2 and H2 gas detection. The exemplary sensing system includes an aperiodic fiber grating with an alternative refractive index modulation for such multi-function sensing and determination. Fabrication of such quasiperiodic grating structures can be made with point-by-point UV laser inscribing, diamond saw micromachining, and phase mask-based coating and chemical etching methods. In the exemplary embodiment, simultaneous detections on multi-parameter can be distributed, but not limited, in gas/steam turbine exhaust, in combustion and compressor, and in coal fired boilers etc.

Abstract: A fiber gas sensor includes a fiber core with first and second refractive index periodic modulated grating structures having different amplitude modulation profiles positioned about the fiber core. A fiber cladding is positioned about the first and second refractive index periodic modulated grating structures. A sensing layer is positioned about the fiber cladding of one of the refractive index periodic modulated grating structures. The sensing layer includes a sensing material made of a Pd-based alloy, such as nano-PdOx, nano-Pd(x)Au(y)Ni(1-x-y) or nano-Pd/Au/WOx. The fiber gas sensor provides a measurement of localized, temperature-corrected gas concentration and composition from a combustion environment. A reflection-based or a transmission-based sensing system with an array of one or more fiber gas sensors is also described.

Abstract: A system and a method for decreasing a rate of slag formation at predetermined locations in a boiler system are provided. The boiler system has a plurality of burners, a plurality of slag detection sensors, a plurality of temperature sensors and a plurality of CO sensors disposed therein. The system determines locations within the boiler system that have relatively high slag thickness levels utilizing the plurality of slag detection sensors and then adjusts A/F ratios or mass flows of burners affecting those locations, or adds slag reducing additives to the burners affecting those locations, to decrease a rate of slag formation at the locations, utilizing signals from the plurality of slag detection sensor, the plurality of temperature sensors, and the plurality of CO sensors.

Abstract: A system, a method, and an article of manufacture for adjusting temperature levels in predetermined locations in a boiler system are provided. The boiler system has a plurality of burners and a plurality of temperature sensors and CO sensors disposed therein. The system determines locations within the boiler system that have relatively high temperature levels utilizing the plurality of temperature sensors and then adjusts A/F ratios of burners affecting those locations to decrease the temperature levels at the locations while maintaining CO levels at or below a threshold level.

Abstract: A sensing module positioned about an optical fiber cable having a long axis. The optical fiber includes a core that transmits light through the optical fiber cable. The sensing module includes a first short-period fiber grating positioned about the core. A second short-period fiber grating is positioned about the core and at a distance along the long axis with respect to the first short-period fiber grating. At least one of a long-aperiod fiber grating and a long-period fiber grating is positioned between the first short-period fiber grating and the second short-period fiber grating. A fiber cladding is positioned around the long-period grating and/or the long-aperiod grating of the sensing module. A sensing skin is positioned about the fiber cladding and includes a chemical gas active material.

Abstract: A method, system and software for reducing combustion chamber to chamber variation in a multiple-combustion chamber turbine system comprising sensing dynamic combustion pressure tones emitted from combustion chambers in a multiple combustion chamber turbine and determining a combustion chamber stratification index for the combustion chambers from the dynamic combustion pressure tones emitted for the combustion chambers to record and/or tune combustion chamber performance variations in the multiple-chamber combustion turbine system.

Abstract: In accordance with one exemplary embodiment the invention provides a multi-parameter fiber optic sensing system with an aperiodic sapphire fiber grating as sensing element for simultaneous temperature, strain, NOx, CO, O2 and H2 gas detection. The exemplary sensing system includes an aperiodic fiber grating with an alternative refractive index modulation for such multi-function sensing and determination. Fabrication of such quasiperiodic grating structures can be made with point-by-point UV laser inscribing, diamond saw micromachining, and phase mask-based coating and chemical etching methods. In the exemplary embodiment, simultaneous detections on multi-parameter can be distributed, but not limited, in gas/steam turbine exhaust, in combustion and compressor, and in coal fired boilers etc.

Abstract: A sensing module positioned about an optical fiber cable having a long axis. The optical fiber includes a core that transmits light through the optical fiber cable. The sensing module includes a first short-period fiber grating positioned about the core. A second short-period fiber grating is positioned about the core and at a distance along the long axis with respect to the first short-period fiber grating. At least one of a long-aperiod fiber grating and a long-period fiber grating is positioned between the first short-period fiber grating and the second short-period fiber grating. A fiber cladding is positioned around the long-period grating and/or the long-aperiod grating of the sensing module. A sensing skin is positioned about the fiber cladding and includes a chemical gas active material.