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Jung, D., Ng, K. Y., Frisk, E. & Krysander, M. (2018). Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation. Control Engineering Practice, 80, 146-156
Open this publication in new window or tab >>Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation
2018 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 80, p. 146-156Article in journal (Refereed) Published
Abstract [en]

Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Fault diagnosis, Fault isolation, Machine learning, Artificial intelligence, Classification
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-151296 (URN)10.1016/j.conengprac.2018.08.013 (DOI)000447483500014 ()
Note

Funding agencies: Volvo Car Corporation in Gothenburg, Sweden

Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2019-09-23
Voronov, S., Frisk, E. & Krysander, M. (2018). Lead-acid battery maintenance using multilayer perceptron models. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM): . Paper presented at 2018 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-8).
Open this publication in new window or tab >>Lead-acid battery maintenance using multilayer perceptron models
2018 (English)In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

Predictive maintenance of components has the potential to significantly reduce costs for maintenance and to reduce unexpected failures. Failure prognostics for heavy-duty truck lead-acid batteries is considered with a multilayer perceptron (MLP) predictive model. Data used in the study contains information about how approximately 46,000 vehicles have been operated starting from the delivery date until the date when they come to the workshop. The model estimates a reliability and lifetime probability function for a vehicle entering a workshop. First, this work demonstrates how heterogeneous data is handled, then the architectures of the MLP models are discussed. Main contributions are a battery maintenance planning method and predictive performance evaluation based on reliability and lifetime functions, a new model for reliability function when its true shape is unknown, the improved objective function for training MLP models, and handling of imbalanced data and comparison of performance of different neural network architectures. Evaluation shows significant improvements of the model compared to more simple, time-based maintenance plans.

Keywords
lead acid batteries, neural nets, preventive maintenance, reliability, lead-acid battery maintenance, MLP models, neural network architectures, reliability function, lifetime functions, predictive performance evaluation, battery maintenance planning method, heterogeneous data, lifetime probability function, multilayer perceptron predictive model, heavy-duty truck lead-acid batteries, failure prognostics, predictive maintenance, simple time-based maintenance plans, improved objective function, Batteries, Histograms, Maintenance engineering, Data models, Neural networks, Conferences
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:liu:diva-163289 (URN)10.1109/ICPHM.2018.8448472 (DOI)
Conference
2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-01-24Bibliographically approved
Jung, D., Dong, Y., Frisk, E., Krysander, M. & Biswas, G. (2018). Sensor selection for fault diagnosis in uncertain systems. International Journal of Control, 1-11
Open this publication in new window or tab >>Sensor selection for fault diagnosis in uncertain systems
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2018 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, p. 1-11Article in journal (Refereed) Epub ahead of print
Abstract [en]

Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Fault diagnosis, fault detection and isolation, sensor selection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117176 (URN)10.1080/00207179.2018.1484171 (DOI)
Note

The previous status of this article was Manuscript.

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2019-09-23Bibliographically approved
Jakobsson, E., Frisk, E., Pettersson, R. & Krysander, M. (2017). Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors. In: Anibal Bregon and Matthew J. Daigle (Ed.), PHM 2017. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017, St. Petersburg, Florida, USA, October 2–5, 2017: . Paper presented at annual conference of the prognostics and health management society 2017, PHM17, October 2-5, St. Petersburg, Florida, USA (pp. 98-107). phmSociety
Open this publication in new window or tab >>Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors
2017 (English)In: PHM 2017. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017, St. Petersburg, Florida, USA, October 2–5, 2017 / [ed] Anibal Bregon and Matthew J. Daigle, phmSociety , 2017, p. 98-107Conference paper, Published paper (Refereed)
Abstract [en]

The life and condition of a MT65 mine truck frame is to a large extent related to how the machine is used. Damage from different stress cycles in the frame are accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors used. To make a monitoring system cheap and robust enough for a mining application, a small number of robust sensors are preferred rather than a multitude of local sensors such as strain gauges. The main question to be answered is whether a low number of robust on-board sensors can give the required information to recreate stress signals at various locations of the frame. Also the choice of sensors among many different locations and kinds are considered. A final question is whether the data could also be used to estimate road condition. By using accelerometer, gyroscope and strain gauge data from field tests of an Atlas Copco MT65 mine truck, coherence and Lasso-regression were evaluated as means to select which signals to use. ARX-models for stress estimation were created using the same data. By simulating stress signals using the models, rain flow counting and damage accumulation calculations were performed. The results showed that a low number of on-board sensors like accelerometers and gyroscopes could give enough information to recreate some of the stress signals measured. Together with a linear model, the estimated stress was accurate enough to evaluate the accumulated fatigue damage in a mining truck. The accumulated damage was also used to estimate the condition of the road on which the truck was traveling. To make a useful road monitoring system some more work is required, in particular regarding how vehicle speed influences damage accumulation.

Place, publisher, year, edition, pages
phmSociety, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-152214 (URN)9781936263264 (ISBN)
Conference
annual conference of the prognostics and health management society 2017, PHM17, October 2-5, St. Petersburg, Florida, USA
Funder
Wallenberg Foundations
Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2019-11-20Bibliographically approved
Polverino, P., Frisk, E., Jung, D., Krysander, M. & Pianese, C. (2017). Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems. Journal of Power Sources, 357, 26-40
Open this publication in new window or tab >>Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems
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2017 (English)In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 357, p. 26-40Article in journal (Refereed) Published
Abstract [en]

The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Polymer Electrolyte Membrane Fuel Cell, Model-based diagnosis, Fault detection, Fault isolation, Residual generation, Algorithm design
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-137770 (URN)10.1016/j.jpowsour.2017.04.089 (DOI)000403457000004 ()2-s2.0-85018962975 (Scopus ID)
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2019-09-23Bibliographically approved
Jung, D., Frisk, E. & Krysander, M. (2016). A flywheel error compensation algorithm for engine misfire detection. Control Engineering Practice, 47, 37-47
Open this publication in new window or tab >>A flywheel error compensation algorithm for engine misfire detection
2016 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 47, p. 37-47Article in journal (Refereed) Published
Abstract [en]

A commonly used signal for engine misfire detection is the crankshaft angular velocity measured at the flywheel. However, flywheel manufacturing errors result in vehicle-to-vehicle variations in the measurements and have a negative impact on the misfire detection performance, where the negative impact is quantified for a number of vehicles. A misfire detection algorithm is proposed with flywheel error adaptation in order to increase robustness and reduce the number of mis-classifications. Since the available computational power is limited in a vehicle, a filter with low computational load, a Constant Gain Extended Kalman Filter, is proposed to estimate the flywheel errors. Evaluations using measurements from vehicles on the road show that the number of mis-classifications is significantly reduced when taking the estimated flywheel errors into consideration.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117177 (URN)10.1016/j.conengprac.2015.12.009 (DOI)000370091900004 ()
Note

Funding agencies:The work is partially supported by the Swedish Research Council within the Linnaeus Center CADICS.

Vid tiden för disputation förelåg publikationen endast som manuskript

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2019-09-23Bibliographically approved
Jung, D., Khorasgani, H., Frisk, E., Krysander, M. & Biswas, G. (2015). Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems. In: Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15: . Paper presented at 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15, 2-4 September, Paris, FRANCE (pp. 1289-1296). Elsevier, 48(21)
Open this publication in new window or tab >>Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems
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2015 (English)In: Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15, Elsevier, 2015, Vol. 48, no 21, p. 1289-1296Conference paper, Published paper (Refereed)
Abstract [en]

Most model-based diagnosis approaches reported in the literature adopt a generic architecture and approach. However, the fault hypotheses generated by these methods may differ. This is not only due to the methods, but also on the basic assumptions made by different diagnostic algorithms on fault manifestation and evolution. While comparing different diagnosis approaches, the assumptions made in each case will have a significant effect on fault diagnosability performance and must therefore also be taken into consideration. Thus, to make a fair comparison, the different approaches should be designed based on the same assumptions. This paper studies the relation between a set of commonly made assumptions and fault isolability performance in order to compare different diagnosis approaches. As a case study, five developed diagnosis systems for a wind turbine benchmark problem are evaluated to analyze the type of assumptions that are applied in the different designs.

Place, publisher, year, edition, pages
Elsevier, 2015
Series
IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X ; Vol. 48, Issue 21
Keywords
Model-based diagnosis, fault detection and isolation, fault diagnosability analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-137772 (URN)10.1016/j.ifacol.2015.09.703 (DOI)2-s2.0-84992486744 (Scopus ID)
Conference
9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15, 2-4 September, Paris, FRANCE
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2019-09-23Bibliographically approved
Jung, D., Eriksson, L., Frisk, E. & Krysander, M. (2015). Development of misfire detection algorithm using quantitative FDI performance analysis. Control Engineering Practice, 34, 49-60
Open this publication in new window or tab >>Development of misfire detection algorithm using quantitative FDI performance analysis
2015 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 34, p. 49-60Article in journal (Refereed) Published
Abstract [en]

A model-based misfire detection algorithm is proposed. The algorithm is able to detect misfires and identify the failing cylinder during different conditions, such as cylinder-to-cylinder variations, cold starts, and different engine behavior in different operating points. Also, a method is proposed for automatic tuning of the algorithm based on training data. The misfire detection algorithm is evaluated using data from several vehicles on the road and the results show that a low misclassification rate is achieved even during difficult conditions. (C) 2014 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Misfire detection; Fault diagnosis; Fault detection and isolation; Kullback-Leibler divergence; Pattern recognition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-114011 (URN)10.1016/j.conengprac.2014.10.001 (DOI)000347599500005 ()
Note

Funding Agencies|Volvo Car Corporation; Swedish Research Council within the Linnaeus Center CADICS

Available from: 2015-02-06 Created: 2015-02-05 Last updated: 2019-09-23
Jung, D., Frisk, E. & Krysander, M. (2015). Quantitative isolability analysis of different fault modes. In: Didier Maquin (Ed.), 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, 2–4 September 2015: Proceedings. Paper presented at 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, France, 2–4 September 2015 (pp. 1275-1282). Elsevier, 48(21)
Open this publication in new window or tab >>Quantitative isolability analysis of different fault modes
2015 (English)In: 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, 2–4 September 2015: Proceedings / [ed] Didier Maquin, Elsevier, 2015, Vol. 48(21), p. 1275-1282Conference paper, Published paper (Refereed)
Abstract [en]

To be able to evaluate quantitative fault diagnosability performance in model-based diagnosis is useful during the design of a diagnosis system. Different fault realizations are more or less likely to occur and the fault diagnosis problem is complicated by model uncertainties and noise. Thus, it is not obvious how to evaluate performance when all of this information is taken into consideration. Four candidates for quantifying fault diagnosability performance between fault modes are discussed. The proposed measure is called expected distinguishability and is based of the previous distinguishability measure and two methods to compute expected distinguishability are presented.

Place, publisher, year, edition, pages
Elsevier, 2015
Series
IFAC-PapersOnLine, ISSN 2405-8963
Keywords
Fault detection and isolation, quantitative diagnosability analysis, Kullback-Leibler divergence
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117175 (URN)10.1016/j.ifacol.2015.09.701 (DOI)
Conference
9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, France, 2–4 September 2015
Note

At the time for thesis presentation publication was in status: Manuscript

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2019-09-23Bibliographically approved
Sundström, C. & Krysander, M. (2015). Smart Energy Usage for Vehicle Charging and House Heating. In: : . Paper presented at 4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM 2015 — Columbus, Ohio, USA, 23-26 August 2015 (pp. 224-229). , 48
Open this publication in new window or tab >>Smart Energy Usage for Vehicle Charging and House Heating
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In northern Europe the electricity price is set by hourly rates one day in advance. The price fluctuates due to supply and demand, and these fluctuations are expected to increase when solar and wind power are increased in the energy system. The potential in cost reduction for heating a house and charging of an electrified vehicle by using a smart energy management system in a household is investigated. Dynamic programming is used and a simulation study of a household in Sweden comparing this optimal control scheme with a heuristic controller is carried out. The time frame in the study is one year and a novel way of handling the fact that the vehicle is disconnected from the grid at some times is developed. A plug-in hybrid electric vehicle is considered, but the methodology is the same also for pure electric vehicles. It is found that the potential in energy cost reduction for house heating and vehicle charging is significant and that using a smart energy management system is a promising path of cost reduction, especially with the introduction of electrified vehicles. 

National Category
Energy Systems Energy Engineering
Identifiers
urn:nbn:se:liu:diva-122460 (URN)10.1016/j.ifacol.2015.10.032 (DOI)
Conference
4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM 2015 — Columbus, Ohio, USA, 23-26 August 2015
Available from: 2015-11-03 Created: 2015-11-03 Last updated: 2019-09-23
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4965-1077

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