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  • 1.
    Frisk, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Larsson, Emil
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Data-driven Lead-Acide Battery Prognostics Using Random Survival Forests2014In: PMH 2014. Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA / [ed] Mathew J. Daigle and Anibal Bregon, PMH Society , 2014, p. 92-101Conference paper (Refereed)
    Abstract [en]

    Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is a case-study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data-driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet management data including 291 variables from 33 603 vehicles from 5 different European markets. The data is a mix of numerical values such as temperatures and pressures, together with histograms and categorical data such as battery mount point. Implementation aspects are discussed such as how to include histogram data and how to reduce the computational complexity by reducing the number of variables. Finally, battery lifetime predictions are computed and evaluated on recorded data from Scania's fleet-management system.

  • 2.
    Larsson, Emil
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Diagnosis and Supervision of Industrial Gas Turbines2012Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Monitoring of industrial gas turbines is of vital importance, since it gives valuable information for the customer about maintenance, performance, and process health. The performance of an industrial gas turbine degrades gradually due to factors such as environment air pollution, fuel content, and ageing to mention some of the degradation factors. The compressor in the gas turbine is especially vulnerable against contaminants in the air since these particles are stuck at the rotor and stator surface. The loss in compressor performance, due to fouling, can partially be restored by an on-line/off-line compressor wash. If the actual health state of the gas turbine is known, it is possible to efficiently plan the service and maintenance and thereby reduce the environmental impact and the fuel cost for the customer.

    A thermodynamic gas turbine modeling package, called GTLib, is developed in the equation-based object-oriented modeling language Modelica. Using the GTLib package, a gas turbine model can be constructed. The gas turbine model can be used for performance calculation and as a base when diagnosis tests are generated. These tests can be used in a diagnosis and supervision system to detect compressor fouling and abrupt sensor faults. One of the benefits with using GTLib is the ability to model a lean stoichiometric combustion at different air/fuel ratio. Using the air/fuel ratio concept, an arbitrary number of gas species in the in-coming air can be considered. The number of equations is reduced if the air/fuel ratio concept is considered instead of modeling each gas species separately. The difference in the number of equations is significant if many gas species are considered.

    When the gas turbine components deteriorate, a mismatch between the nominal performance model and the measurements increase. To handle this, the gas turbine model is augmented with a number of estimation parameters. These estimation parameters are used to detect slow deterioration in the gas turbine components and are estimated with a Constant Gain Extended Kalman Filter (CGEKF). The state estimator is chosen using structural methods before an index reduction of the model is performed. Experimental data is investigated and it is shown that the performance degradation due to compressor fouling can be estimated. After the compressor is washed, the performance of the compressor is partially restored. An abrupt sensor fault of 1% of the nominal value is introduced in the discharge temperature of the compressor. The sensor fault can be detected using the CUSUM algorithm for change detection.

    Finally, the overall thesis contribution is the calculation chain from a simulation model used for performance calculation to a number of test quantities used in a diagnosis and supervision system. Since the considered gas turbine model is a large non-linear DAE model that has unobservable state variables, the test construction procedure is automatically performed with developed parsers.

  • 3.
    Larsson, Emil
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Model Based Diagnosis and Supervision of Industrial Gas Turbines2014Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Supervision of performance in gas turbine applications is important in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain good diagnosis performance it is important to have tests which are based on models with high accuracy. A main contribution of the thesis is a systematic design procedure to construct a fault detection and isolation (FDI) system which is based on complex nonlinear models.These models are preliminary used for simulation and performance evaluations. Thus, is it possible to use thesemodels also in the FDI-system and whichmodel parts are necessary to consider in the test design? To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package GTLib is developed. Using the GTLib framework, a gas turbine diagnosismodel is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis models are carefully selected.These equations are then used to implement a Constant Gain Extended Kalman filter (CGEKF) based test quantity.The number of equations and variables which the test quantity is based on is significantly reduced compared to the original reference model.The test quantity is used in the FDI-system to supervise the performance and the turbine inlet temperature which is used in the controller. An evaluation is performed using experimental data from a gas turbine site.The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. When the FDI-system is augmented with more test quantities it is possible to diagnose sensor and actuator faults at the same time the performance is supervised. Slow varying sensor and actuator bias faults are difficult diagnose since they appear in a similar manner as the performance deterioration, but the FDI-system has the ability to detect these faults. Finally, the proposed model based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.

  • 4.
    Larsson, Emil
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Eriksson, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Fault Isolation for an Industrial Gas Turbine with a Model-Based Diagnosis Approach2010In: Proceedings of ASME Turbo Expo, ASME Press, 2010, p. 89-98Conference paper (Refereed)
    Abstract [en]

    Model based diagnosis and supervision of industrial gas turbines are studied. Monitoring of an industrial gas turbine is important as it gives valuable information for the customer about service performance and process health. The overall objective of the paper is to develop a systematic procedure for modelling and design of a model based diagnosis system, where each step in the process can be automated and implemented using available software tools. A new Modelica gas media library is developed, resulting in a significant model size reduction compared to if standard Modelica components are used. A systematic method is developed that, based on the diagnosis model, extracts relevant parts of the model and transforms it into a form suitable for standard observer design techniques. This method involves techniques from simulation of DAE models and a model reduction step. The size of the final diagnosis model is 20% of the original model size. Combining the modeling results with fault isolation techniques, simultaneous isolation of sensor faults and fault tolerant health parameter estimation is achieved.

  • 5.
    Larsson, Emil
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Eriksson, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Fault Tolerant Supervision of an Industrial Gas Turbine2013In: Proceedings of ASME Turbo Expo, 2013Conference paper (Refereed)
    Abstract [en]

    Supervision of the performance of an industrial gas turbine is important since it gives valuable information of the process health and makes efficient determination of compressor wash intervals possible. Slowly varying sensor faults can easily be misinterpreted as performance degradations and result in an unnecessary compressor wash. Here, a diagnostic algorithm is carefully combined with non-linear state observers to achieve fault tolerant performance estimation. The proposed approach is evaluated in an experimental case study with six months of measurement data from a gas turbine site. The investigation shows that faults in all gas path instrumentation sensors are detectable and isolable. A key result of the case study is the ability to detect and isolate a slowly varying sensor fault in the discharge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.charge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.

  • 6.
    Larsson, Emil
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Åslund, Jan
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Eriksson, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Gas Turbine Modeling for Diagnosis and Control2014In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 136, no 7, p. 071601-Article in journal (Refereed)
    Abstract [en]

    The supervision of performance in gas turbine applications is crucial in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain a good diagnosis of performance it is important to have tests which are based on models with high accuracy. A main contribution is a systematic design procedure to construct a fault detection and isolation (FDI) system for complex nonlinear models. To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package (GTLib) is developed. Using the GTLib framework, a gas turbine diagnosis model is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis model are carefully selected. These equations are then used to implement a constant gain extended Kalman filter (CGEKF)-based test quantity. The test quantity is used in the FDI-system to supervise the performance and in the controller to estimate the flame temperature. An evaluation is performed using experimental data from a gas turbine site. The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. Thus, the proposed model-based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.

  • 7.
    Larsson, Emil
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Åslund, Jan
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Eriksson, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques2012In: Proceedings of ASME Turbo Expo, 2011, GT2011, June 6-10, 2011, Vancouver, British Columbia, Canada, ASME Digital Collection , 2012, Vol. 3, p. 487-495, article id GT2011-46825Conference paper (Refereed)
    Abstract [en]

    Monitoring of an industrial gas turbine is important since it gives valuable information for the customer about maintenance, performance and process health. The objective of the paper is to develop a monitoring system for an industrial gas turbine application with a model based diagnosis approach. A constant gain extended Kalman observer is developed. The observer compensates for different ambient conditions such as pressure, temperature and relative humidity, due to the amount of water in the atmosphere. The developed observer, extended with seven health parameters, is automatically constructed from the diagnosis model. These health parameters shall capture deviations in some of the gas path performance parameters such as efficiency, mass flow, turbine inlet area and head loss. The constructed observer is evaluated through a simulation study where the ambient conditions are changed. The considered observer capture the change in different ambient conditions nearly perfect. An observer that does not compensate for different ambient conditions gives an error for about 1–2% for the considered health parameters for the given test case. The constructed observer is also evaluated on measurement data from a mechanical drive site. A degradation in efficiency and mass flow for the compressor due to fouling can be seen in the estimations. After the compressor wash is performed, the degradations for the compressor are partially restored by about 2% which can be seen in the considered health parameters.

  • 8.
    Larsson, Emil
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Åslund, Jan
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Eriksson, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques2011In: Proceedings of ASME Turbo Expo 2011, GT2011, June 6-10, 2011, Vancouver, British Columbia, Canada, ASME Digital Collection , 2011, Vol. 3, p. 487-495, article id GT2011-46825Conference paper (Refereed)
    Abstract [en]

    Monitoring of an industrial gas turbine is important since it gives valuable information for the customer about maintenance, performance and process health. The objective of the paper is to develop a monitoring system for an industrial gas turbine application with a model based diagnosis approach. A constant gain extended Kalman observer is developed. The observer compensates for different ambient conditions such as pressure, temperature and relative humidity, due to the amount of water in the atmosphere. The developed observer, extended with seven health parameters, is automatically constructed from the diagnosis model. These health parameters shall capture deviations in some of the gas path performance parameters such as efficiency, mass flow, turbine inlet area and head loss. The constructed observer is evaluated through a simulation study where the ambient conditions are changed. The considered observer capture the change in different ambient conditions nearly perfect. An observer that does not compensate for different ambient conditions gives an error for about 1-2 % for the considered health parameters for the given test case. The constructed observer is also evaluated on measurement data from a mechanical drive site. A degradation in efficiency and mass flow for the compressor due to fouling can be seen in the estimations. After the compressor wash is performed, the degradations for the compressor are partially restored by about 2 % which can be seen in the considered health parameters.

1 - 8 of 8
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