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Diagnosability analysis and FDI system design for uncertain systems
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Our society depends on advanced and complex technical systems and machines, for example, cars for transportation, industrial robots in production lines, satellites for communication, and power plants for energy production. Consequences of a fault in such a system can be severe and result in human casualties, environmentally harmful emissions, high repair costs, or economical losses caused by unexpected stops in production lines. Thus, a diagnosis system is important, and in some applications also required by legislations, to monitor the system health in order to take appropriate preventive actions when a fault occurs. Important properties of diagnosis systems are their capability of detecting and identifying faults, i.e., their fault detectability and isolability performance.

This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze diagnosability performance given a mathematical model of the system to be monitored before a diagnosis system is developed. A measure of fault diagnosability performance, called distinguishability, is proposed based on the Kullback-Leibler divergence. For linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. Distinguishability is used to analyze fault detectability and isolability performance of a non-linear mean value engine model of gas flows in a heavy duty diesel engine by linearizing the model around different operating points.

It is also shown how distinguishability is used for determine sensor placement, i.e, where sensors should be placed in a system to achieve a required fault diagnosability performance. The sensor placement problem is formulated as an optimization problem, where minimum required diagnosability performance is used as a constraint. Results show that the required diagnosability performance greatly affects which sensors to use, which is not captured if not model uncertainties and measurement noise are taken into consideration.

Another problem considered here is the on-line sequential test selection problem. Distinguishability is used to quantify the performance of the different test quantities. The set of test quantities is changed on-line, depending on the output of the diagnosis system. Instead of using all test quantities the whole time, changing the set of active test quantities can be used to maintain a required diagnosability performance while reducing the computational cost of the diagnosis system. Results show that the number of used test quantities can be greatly reduced while maintaining a good fault isolability performance.

A quantitative diagnosability analysis has been used during the design of an engine misfire detection algorithm based on the estimated torque at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance. Related to the misfire detection problem, a flywheel angular velocity model for misfire simulation is presented. An evaluation of the misfire detection algorithm show results of good detection performance as well as low false alarm rate.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. , 19 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1584
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-89947Local ID: LIU-TEK-LIC-2013:18ISBN: 978-91-7519-652-7 (print)OAI: oai:DiVA.org:liu-89947DiVA: diva2:610541
Presentation
2013-04-05, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2013-03-12 Created: 2013-03-12 Last updated: 2013-05-07Bibliographically approved
List of papers
1. A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Open this publication in new window or tab >>A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 6, 1591-1600 p.Article in journal (Refereed) Published
Abstract [en]

Analyzing fault diagnosability performance for a given model, before developing a diagnosis algorithm, can be used to answer questions like “How difficult is it to detect a fault fi?” or “How difficult is it to isolate a fault fi from a fault fj?”. The main contributions are the derivation of a measure, distinguishability, and a method for analyzing fault diagnosability performance of discrete-time descriptor models. The method, based on the Kullback–Leibler divergence, utilizes a stochastic characterization of the different fault modes to quantify diagnosability performance. Another contribution is the relation between distinguishability and the fault to noise ratio of residual generators. It is also shown how to design residual generators with maximum fault to noise ratio if the noise is assumed to be i.i.d. Gaussian signals. Finally, the method is applied to a heavy duty diesel engine model to exemplify how to analyze diagnosability performance of non-linear dynamic models.

Place, publisher, year, edition, pages
Elsevier, 2013
Keyword
Fault diagnosability analysis; Fault detection and isolation; Model-based diagnosis
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89941 (URN)10.1016/j.automatica.2013.02.045 (DOI)000319540500007 ()
Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2017-12-06Bibliographically approved
2. Using quantitative diagnosability analysis for optimal sensor placement
Open this publication in new window or tab >>Using quantitative diagnosability analysis for optimal sensor placement
2012 (English)In: Proceedings of the 8th IFAC Safe Process, Mexico City, Mexico / [ed] Carlos Manuel Astorga-Zaragoza, Arturo Molina Gutierrez and Adriana Aguilera-Gonzalez, Curran Associates, Inc., 2012, 940-945 p.Conference paper, Published paper (Refereed)
Abstract [en]

A good placement of sensors is crucial to get good performance in detecting and isolating faults. Here, the sensor placement problem is cast as a minimal cost optimization problem. Previous works have considered this problem with qualitative detectability and isolability specifications. A key contribution here is that quantified detectability and isolability performance is considered in the optimization formulation. The search space for the posed optimization problem is exponential in size, and to handle complexity a greedy optimization algorithm that compute optimal sensor positions is proposed. Two examples illustrate how the optimal solution depends on the required quantified diagnosability performance and the results are compared to the solutions using a deterministic method.

Place, publisher, year, edition, pages
Curran Associates, Inc., 2012
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89942 (URN)10.3182/20120829-3-MX-2028.00196 (DOI)978-390282309-0 (ISBN)
Conference
8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2012; Mexico City; Mexico
Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2014-11-13Bibliographically approved
3. A sequential test selection algorithm for fault isolation
Open this publication in new window or tab >>A sequential test selection algorithm for fault isolation
2012 (English)In: Proceedings of the 10th European Workshop on Advanced Control and Diagnosis, ACD 2012, Copenhagen, Denmark, 2012Conference paper, Published paper (Refereed)
Abstract [en]

A sequential test selection algorithm is proposed which updates the set of active test quantities depending on the present minimal candidates. By sequentially updating the set of active test quantities, computational time and memory usage can be reduced. If test quantities are generated on-line, a sequential test selection algorithm gives information about which test quantities that should be created. The test selection problem is defined as an optimization problem where a set of active test quantities is chosen such that the cost is minimized while the set fulfills a required minimum detectability and isolability performance. A quantitative diagnosability measure, distinguishability, is used to quantify diagnosability performance of test quantities. The proposed test selection algorithm is applied to a DC-circuit where the diagnosis algorithm generates residuals on-line. Experiments show that the sequential test selection algorithm can significantly reduce the number of active test quantities during a scenario and still be able to identify the true faults.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89943 (URN)
Conference
10th European Workshop on Advanced Control and Diagnosis, ACD 2012, November 8-9, Copenhagen, Denmark
Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2013-03-12Bibliographically approved
4. Flywheel angular velocity model for misfire simulation
Open this publication in new window or tab >>Flywheel angular velocity model for misfire simulation
2013 (English)Manuscript (preprint) (Other academic)
Abstract [en]

A flywheel angular velocity model for misfire and disturbance simulation is presented. Applications of the model are, for example, initial parameter calibration or robustness analysis of misfire detection algorithms. An analytical model of cylinder pressure is used to model cylinder torque and a multi-body model is used to model crankshaft and driveline oscillations. Different types of disturbances, such as cylinder variations, changes in auxiliary load, and flywheel manufacturing errors can be injected in the model. A qualitative validation of the model shows that simulated angular velocity captures the amplitude and oscillatory behavior of real measurements and the effects of different types of disturbances, e.g. misfire and flywheel manufacturing errors.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89944 (URN)
Available from: 2013-03-12 Created: 2013-03-12 Last updated: 2013-03-12
5. Analysis and optimization with the Kullback-Leibler divergence for misfire detection using estimated torque
Open this publication in new window or tab >>Analysis and optimization with the Kullback-Leibler divergence for misfire detection using estimated torque
2013 (English)Report (Other academic)
Abstract [en]

Engine misfire detection is an important part of the On-Board Diagnostics (OBDII) legislations to reduce exhaust emissions and avoid damage to the catalytic converters. The flywheel angular velocity signal is analyzed, investigating how to use the signal in order to best detect misfires. An algorithm for engine misfire detection is proposed based on the flywheel angular velocity signal. The flywheel signal is used to estimate the torque at the flywheel and a test quantity is designed by weighting and thresholding the samples of estimated torque related to one combustion. During the development process, the Kullback-Leibler divergence is used to analyze the ability to detect a misfire given a test quantity and how the misfire detectability performance varies depending on, e.g., load and speed. The Kullback-Leibler divergence is also used for parameter optimization to maximize the difference between misfire data and fault-free data. Evaluation shows that the proposed misfire detection algorithm is able to have a low probability of false alarms while having a low probability of missed detections.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 36 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3057
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89946 (URN)LiTH-ISY-R-3057 (ISRN)
Available from: 2013-03-12 Created: 2013-03-12 Last updated: 2017-01-27Bibliographically approved

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