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Frisk, Erik
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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: 2018-10-30
Hockerdal, E., Frisk, E. & Eriksson, L. (2018). Real-time performance of DAE and ODE based estimators evaluated on a diesel engine. Science China Information Sciences, 61(7), Article ID 70202.
Open this publication in new window or tab >>Real-time performance of DAE and ODE based estimators evaluated on a diesel engine
2018 (English)In: Science China Information Sciences, ISSN 1674-733X, E-ISSN 1869-1919, Vol. 61, no 7, article id 70202Article in journal (Refereed) Published
Abstract [en]

Computation and sampling time requirements for real-time implementation of observers is studied. A common procedure for state estimation and observer design is to have a system model in continuous time that is converted to sampled time with Euler forward method and then the observer is designed and implemented in sampled time in the real time system. When considering state estimation in real time control systems for production there are often limited computational resources. This becomes especially apparent when designing observers for stiff systems since the discretized implementation requires small step lengths to ensure stability. One way to reduce the computational burden, is to reduce the model stiffness by approximating the fast dynamics with instantaneous relations, transforming an ordinary differential equations (ODE) model into a differential algebraic equation (DAE) model. Performance and sampling frequency limitations for extended Kalman filter (EKF)s based on both the original ODE model and the reduced DAE model are here analyzed and compared for an industrial system. Furthermore, the effect of using backward Euler instead of forward Euler when discretizing the continuous time model is also analyzed. The ideas are evaluated using measurement data from a diesel engine. The engine is equipped with throttle, exhaust gas recirculation (EGR), and variable geometry turbines (VGT) and the stiff model dynamics arise as a consequence of the throttle between two control volumes in the air intake system. The process of simplifying and modifying the stiff ODE model to a DAE model is also discussed. The analysis of the computational effort shows that even though the ODE, for each time-update, is less computationally demanding than the resulting DAE, an EKF based on the DAE model achieves better estimation performance than one based on the ODE with less computational effort. The main gain with the DAE based EKF is that it allows increased step lengths without degrading the estimation performance compared to the ODE based EKF.

Place, publisher, year, edition, pages
SCIENCE PRESS, 2018
Keywords
estimation; learning; DAE; ODE; EKF; observability; real-time
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-149704 (URN)10.1007/s11432-017-9332-6 (DOI)000436183000009 ()
Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-08-14
Jung, D. & Frisk, E. (2018). Residual selection for fault detection and isolation using convex optimization. Automatica, 97, 143-149
Open this publication in new window or tab >>Residual selection for fault detection and isolation using convex optimization
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 97, p. 143-149Article in journal (Refereed) Published
Abstract [en]

In model-based diagnosis there are often more candidate residual generators than what is needed and residual selection is therefore an important step in the design of model-based diagnosis systems. The availability of computer-aided tools for automatic generation of residual generators have made it easier to generate a large set of candidate residual generators for fault detection and isolation. Fault detection performance varies significantly between different candidates due to the impact of model uncertainties and measurement noise. Thus, to achieve satisfactory fault detection and isolation performance, these factors must be taken into consideration when formulating the residual selection problem. Here, a convex optimization problem is formulated as a residual selection approach, utilizing both structural information about the different residuals and training data from different fault scenarios. The optimal solution corresponds to a minimal set of residual generators with guaranteed performance. Measurement data and residual generators from an internal combustion engine test-bed is used as a case study to illustrate the usefulness of the proposed method.

Place, publisher, year, edition, pages
Pergamon Press, 2018
Keywords
Fault detection and isolation, Feature selection, Model-based diagnosis, Convex optimization, Computer-aided design tools
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-151295 (URN)10.1016/j.automatica.2018.08.006 (DOI)000447568400016 ()2-s2.0-85051683130 (Scopus ID)
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-11-09Bibliographically 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: 2018-07-14Bibliographically 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: Proceedings of annual conference of the prognostics and health management society 2017, PHM17: . Paper presented at annual conference of the prognostics and health management society 2017, PHM17 (pp. 98-107).
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: Proceedings of annual conference of the prognostics and health management society 2017, PHM17, 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.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-152214 (URN)978-1-936263-26-4 (ISBN)
Conference
annual conference of the prognostics and health management society 2017, PHM17
Funder
Wallenberg Foundations
Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-10-31
Nyberg, P., Frisk, E. & Nielsen, L. (2017). Driving Cycle Equivalence and Transformation. IEEE Transactions on Vehicular Technology, 66(3), 1963-1974
Open this publication in new window or tab >>Driving Cycle Equivalence and Transformation
2017 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 66, no 3, p. 1963-1974Article in journal (Refereed) Published
Abstract [en]

There is a current strong trend where driving cycles are used extensively in vehicle design, especially for calibration and tuning of all powertrain systems for control and diagnosis. In such situations it is essential to capture real driving, and therefore using only a few driving cycles would lead to the risk that a test or a design would be tailored to details in a specific driving cycle. Consequently there are now widespread activities using techniques from statistics, big data and mission modeling to address these issues. For all such methods there is an important final step to calibrate a representative cycle to adhere to fair propulsion requirements on the driven wheels over a cycle. For this a general methodology has been developed, applicable to a wide range of problems involving driving cycle transformations. It is based on a definition of equivalence for driving cycles that loosely speaking defines being similar without being the same. Based on this, a set of algorithms are developed to transform a given driving cycle into an equivalent one, or into a cycle with given equivalence measure. The transformations are effectively handled as a nonlinear program that is solved using general purpose optimization techniques. The proposed method is general and a wide range of constraints can be used.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
mean tractive force, nonlinear programming, numerical optimization, vehicle design, vehicle propulsion
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-118104 (URN)10.1109/TVT.2016.2582079 (DOI)000396401400006 ()
Note

Funding agencies|Swedish Hybrid Vehicle Centre; Linnaeus Center CADICS

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

Available from: 2015-05-21 Created: 2015-05-21 Last updated: 2018-01-11Bibliographically 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: 2017-07-05Bibliographically 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: 2018-09-17Bibliographically approved
Sundström, C., Frisk, E. & Nielsen, L. (2016). Diagnostic Method Combining the Lookup Tables and Fault Models Applied on a Hybrid Electric Vehicle. IEEE Transactions on Control Systems Technology, 24(3), 1109-1117
Open this publication in new window or tab >>Diagnostic Method Combining the Lookup Tables and Fault Models Applied on a Hybrid Electric Vehicle
2016 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 24, no 3, p. 1109-1117Article in journal (Refereed) Published
Abstract [en]

A common situation in industry is to store measurements for different operating points in the lookup tables, often called maps. They are used in many tasks, e.g., in control and estimation, and therefore considerable investments in engineering time are spent in measuring them which usually make them accurate descriptions of the fault-free system. They are thus well suited for fault detection, but, however, such a model cannot give fault isolation since only the fault free behavior is modeled. One way to handle this situation would be also to map all fault cases but that would require measurements for all faulty cases, which would be costly if at all possible. Instead, the main contribution here is a method to combine the lookup model with analytical fault models. This makes good use of all modeling efforts of the lookup model for the fault-free case, and combines it with fault models with reasonable modeling and calibration efforts, thus decreasing the engineering effort in the diagnosis design. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. An extensive simulation study clearly shows that the approach achieves both good fault detectability and isolability performance. A main point is that this is achieved without the need for neither measurements of a faulty system nor detailed physical modeling, thus saving considerable amounts of development time.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016
Keywords
Electric machine; fault detection; fault diagnosis; fault isolation; hybrid electric vehicle (HEV); lookup table
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-128736 (URN)10.1109/TCST.2015.2480008 (DOI)000375273200032 ()
Available from: 2016-05-31 Created: 2016-05-30 Last updated: 2017-11-30
Nyberg, P., Frisk, E. & Nielsen, L. (2016). Using Real-World Driving Databases to Generate Driving Cycles with Equivalence Properties. IEEE Transactions on Vehicular Technology, 65(6), 4095-4105
Open this publication in new window or tab >>Using Real-World Driving Databases to Generate Driving Cycles with Equivalence Properties
2016 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 6, p. 4095-4105Article in journal (Refereed) Published
Abstract [en]

Due to the increasing complexity of vehicle design, understanding driver behavior and driving patterns is becoming increasingly more important. Therefore, a large amount of test driving is performed, which together with recordings of normal driving, results in large databases of recorded drives. A fundamental question is how to make best use of these data to devise driving cycles suitable in the development process of vehicles. One way is to generate driving cycles that are representative for the data or for a suitable subset of the data, e.g., regarding geographical location, driving distance, speed range, or many other possible selection variables. Further, to make a fair comparison on two such driving cycles possible, another fundamental requirement is that they should have similar excitation of the vehicle. A key contribution here is an algorithm that combines the two given objectives. A formulation with Markov processes is used to obtain a condensed and effective characterization of the database and to generate candidate driving cycles (CDCs). In addition to that is a method transforming a candidate to an equivalent driving cycle (EqDC) with desired excitation. The method is a general approach but is here based on the components of the mean tractive force (MTF), and this is motivated by a hardware-in-the-loop experiment showing the strong relevance of these MTF components regarding fuel consumption. The result is a new method that combines the generation of driving cycles using real-world driving cycles with the concept of EqDCs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Drive cycle, equivalence measures, equivalent driving cycle (EqDC), mean tractive force (MTF), specific energy, test procedures, vehicle design, vehicle excitation, vehicle propulsion
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-118101 (URN)10.1109/TVT.2015.2502069 (DOI)000380068500022 ()
Note

Funding agencies|Swedish Hybrid Vehicle Centre; Linnaeus Center CADICS

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

Available from: 2015-05-21 Created: 2015-05-21 Last updated: 2018-01-11Bibliographically approved
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