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Larsson, Emil
Publications (8 of 8) Show all publications
Frisk, E., Krysander, M. & Larsson, E. (2014). Data-driven Lead-Acide Battery Prognostics Using Random Survival Forests. In: Mathew J. Daigle and Anibal Bregon (Ed.), PMH 2014. Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA: . Paper presented at Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA, September 29 - October 2 (pp. 92-101). PMH Society
Open this publication in new window or tab >>Data-driven Lead-Acide Battery Prognostics Using Random Survival Forests
2014 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
PMH Society, 2014
Series
Proceedings, PHM Society, ISSN 2325-0178
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-137776 (URN)978-1-936263-17-2 (ISBN)
Conference
Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA, September 29 - October 2
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2019-09-23Bibliographically approved
Larsson, E., Åslund, J., Frisk, E. & Eriksson, L. (2014). Gas Turbine Modeling for Diagnosis and Control. Journal of engineering for gas turbines and power, 136(7), 071601
Open this publication in new window or tab >>Gas Turbine Modeling for Diagnosis and Control
2014 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME), 2014
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-109175 (URN)10.1115/1.4026598 (DOI)000337938700008 ()
Available from: 2014-08-12 Created: 2014-08-11 Last updated: 2018-01-30Bibliographically approved
Larsson, E. (2014). Model Based Diagnosis and Supervision of Industrial Gas Turbines. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Model Based Diagnosis and Supervision of Industrial Gas Turbines
2014 (English)Doctoral 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. p. 195 including Appedix A and B
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1603
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-106256 (URN)10.3384/diss.diva-106256 (DOI)978-91-7519-312-0 (ISBN)
Public defence
2014-06-12, Visionen, Hus B, Campus Valla, Linköping, 10:15
Supervisors
Available from: 2014-05-16 Created: 2014-04-30 Last updated: 2018-01-30Bibliographically approved
Larsson, E., Åslund, J., Frisk, E. & Eriksson, L. (2013). Fault Tolerant Supervision of an Industrial Gas Turbine. In: Proceedings of ASME Turbo Expo: . Paper presented at ASME 2013 Turbo Expo: Turbine Technical Conference and Exposition (GT2013), June 3-7, 2013, San Antonio, Texas, USA..
Open this publication in new window or tab >>Fault Tolerant Supervision of an Industrial Gas Turbine
2013 (English)In: Proceedings of ASME Turbo Expo, 2013Conference paper, Published 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.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-89299 (URN)10.1115/GT2013-95727 (DOI)978-0-7918-5518-8 (ISBN)
Conference
ASME 2013 Turbo Expo: Turbine Technical Conference and Exposition (GT2013), June 3-7, 2013, San Antonio, Texas, USA.
Available from: 2013-02-25 Created: 2013-02-25 Last updated: 2018-01-30
Larsson, E. (2012). Diagnosis and Supervision of Industrial Gas Turbines. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Diagnosis and Supervision of Industrial Gas Turbines
2012 (English)Licentiate 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. p. 141
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1528
National Category
Control Engineering Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-75985 (URN)978-91-7519-914-6 (ISBN)
Presentation
2012-04-13, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2012-03-23 Created: 2012-03-21 Last updated: 2018-01-30Bibliographically approved
Larsson, E., Åslund, J., Frisk, E. & Eriksson, L. (2012). Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques. In: Proceedings of ASME Turbo Expo, 2011, GT2011, June 6-10, 2011, Vancouver, British Columbia, Canada: . Paper presented at ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition, June 6-10, 2011, Vancouver, British Columbia, Canada (pp. 487-495). ASME Digital Collection, 3, Article ID GT2011-46825.
Open this publication in new window or tab >>Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques
2012 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
ASME Digital Collection, 2012
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-89298 (URN)10.1115/GT2011-46825 (DOI)000320967100047 ()978-0-7918-5463-1 (ISBN)
Conference
ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition, June 6-10, 2011, Vancouver, British Columbia, Canada
Available from: 2013-02-25 Created: 2013-02-25 Last updated: 2018-01-30Bibliographically approved
Larsson, E., Åslund, J., Frisk, E. & Eriksson, L. (2011). Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques. In: Proceedings of ASME Turbo Expo 2011, GT2011, June 6-10, 2011, Vancouver, British Columbia, Canada: . Paper presented at ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition, Controls, Diagnostics and Instrumentation, Education; Electric Power; Microturbines and Small Turbomachinery, Solar Brayton and Rankine Cycle, Vancouver, British Columbia, Canada, June 6–10, 2011 (pp. 487-495). ASME Digital Collection, 3, Article ID GT2011-46825.
Open this publication in new window or tab >>Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques
2011 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
ASME Digital Collection, 2011
Keywords
Industrial gases, Turbines, Patient diagnosis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-137788 (URN)10.1115/GT2011-46825 (DOI)978-0-7918-5463-1 (ISBN)
Conference
ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition, Controls, Diagnostics and Instrumentation, Education; Electric Power; Microturbines and Small Turbomachinery, Solar Brayton and Rankine Cycle, Vancouver, British Columbia, Canada, June 6–10, 2011
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2018-01-30Bibliographically approved
Larsson, E., Åslund, J., Frisk, E. & Eriksson, L. (2010). Fault Isolation for an Industrial Gas Turbine with a Model-Based Diagnosis Approach. In: Proceedings of ASME Turbo Expo: . Paper presented at ASME Turbo Expo 2010: Power for Land, Sea, and Air (GT2010), June 14–18, 2010, Glasgow, UK. (pp. 89-98). ASME Press
Open this publication in new window or tab >>Fault Isolation for an Industrial Gas Turbine with a Model-Based Diagnosis Approach
2010 (English)In: Proceedings of ASME Turbo Expo, ASME Press, 2010, p. 89-98Conference paper, Published 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.

Place, publisher, year, edition, pages
ASME Press, 2010
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-89292 (URN)10.1115/GT2010-22511 (DOI)9780791843987 (ISBN)
Conference
ASME Turbo Expo 2010: Power for Land, Sea, and Air (GT2010), June 14–18, 2010, Glasgow, UK.
Available from: 2013-02-25 Created: 2013-02-25 Last updated: 2018-01-30
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