A survey on diagnostic methods for automotive engines

Mohammadpour, Javad; Franchek, Matthew A.; Grigoriadis, Karolos

doi:10.1177/1468087411422851

Cited by 52 publications

(26 citation statements)

References 142 publications

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“…An overview of misfire detection research is given by Mohammadpour et al (2012). A great majority of misfire detection algorithms are based on looking at the flywheel angular velocity signal since it is severely influenced by the engine torque, see Connolly and Rizzoni (1994); Kiencke (1999); Tinaut et al (2007).…”

Section: Research Motivationmentioning

confidence: 99%

System Identification of an Engine-load Setup Using Grey-box Model

Nickmehr¹

2014

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With the demand for more comfortable cars and reduced emissions, there is an increasing focus on model-based system engineering. Therefore, developing accurate vehicle models has become significantly important. The powertrain system, which transfers the engine torque to the driving wheels, is one of the most important parts of a vehicle. Having a reliable methodology, for modeling and parameter estimation of a powertrain structure, helps predict different kinds of behaviors such as torsional vibration which is beneficial for a number of applications in automotive industry. Examples of such cases are ride quality evaluation and model-based fault detection.This thesis uses the knowledge from the system identification field, which introduces the methods of building mathematical models for dynamical systems based on experimental data, to model the torsional vibration of an engine-load setup. It is a subsystem of the vehicular powertrain and the main source of vibration is the engine fluctuating torque. The challenges are handling a more complicated model structure with a greater number of unknown parameters as well as showing the importance of data information for acquiring better identification performance. Since the engine-load setup is modeled physically here, its state-space equations are available and a grey-box modeling approach can be applied in which the well-known prediction error method is used to estimate the unknown physical parameters. Moreover, a structural identifiability analysis is performed which shows that all of the model parameters are identifiable assuming informative input.Two main aspects are considered to present an appropriate modeling methodology. The first is simplification of the model structure according to frequency range of interest. This is achieved by performing modal shape analysis to obtain how many degrees-of-freedom are necessary at different frequency ranges. The results show that a 7 degrees-of-freedom model can be simplified to a 2 degrees-offreedom structure and still have the desired performance for a specific application such as misfire detection.The second aspect concerns using an appropriate data set, which has the required information for estimation of the unknown parameters. By analyzing the simulation data from a known system, it is shown that the parameters of the 2 degrees-of-freedom model can not be estimated accurately using measurements from a normal combustion data set. However, all the parameters except damping coefficient converge to their true values by using a data set which has misfire in the input torque from the engine. A high estimation variance plus flat loss function indicate that the damping coefficient has no significant influence on the model output and consequently can not be estimated correctly using the available measurements. Thus, to increase the accuracy of the results during estimation on real data, the damping coefficient(s) is assumed to be known. Both the 2 and 7 degrees-of-freedom models are validated against a fresh data set and ...

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Section: Research Motivationmentioning

confidence: 99%

System Identification of an Engine-load Setup Using Grey-box Model

Nickmehr¹

2014

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“…For a comprehensive discussion regarding diagnosis requirements for automotive engines in general, see Mohammadpour et al (2012).…”

Section: Design Of Residual Generatorsmentioning

confidence: 99%

Automotive engine FDI by application of an automated model-based and data-driven design methodology

Svärd

Nyberg

Frisk

et al. 2013

Control Engineering Practice

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Fault detection and isolation (FDI) in automotive diesel engines is important in order to achieve and guarantee low exhaust emissions, high vehicle uptime, and efficient repair and maintenance. This paper illustrates how a set of general methods for model-based sequential residual generation and data-driven statistical residual evaluation can be combined into an automated design methodology. The automated design methodology is then utilized to create a complete FDI-system for an automotive diesel engine. The performance of the obtained FDI-system is evaluated using measurements from road drives and engine testbed experiments. The overall performance of the FDI-system is good in relation to the required design effort. In particular no specific tuning of the FDI-system, or any adaption of the design methodology, were needed. It is illustrated how estimations of the statistical powers of the fault detection tests in the FDI-system can be used to further increase the performance, specifically in terms of fault isolability.

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“…In the automotive industry, the on-board diagnosis (OBDII) legislations require that many systems in a vehicle are monitored on-line to prevent faults that potentially lead to increased emissions, increased fuel consumption, or damage to important parts of the vehicle. An overview of automotive diagnosis research is found in (Mohammadpour et al, 2012). One example of an automotive diagnosis application is engine misfire detection.…”

Section: Design Aspects Of Diagnosis Systemsmentioning

confidence: 99%

“…Misfire detection is an important part of the OBDII legislations in order to reduce exhaust emissions and avoid damage to the catalytic converters. The legislations require that the on-board diagnosis system is able to both detect misfires and identify in which cylinder the misfire occurred (Heywood, 1988;Walter et al, 2007;Mohammadpour et al, 2012). Misfires can be intermittent from one or several cylinders, or persistent from a complete cylinder failure.…”

Section: Design Aspects Of Diagnosis Systemsmentioning

confidence: 99%

Diagnosability performance analysis of models and fault detectors

Jung

2015

Linköping Studies in Science and Technology. Dissertations

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The cover: Graphical illustration of the proposed quantitative fault detectability and isolability performance measure, called distinguishability (Di,j(θ)). Typeset with L A T E X 2εPrinted by LiU-Tryck, Linköping, Sweden 2015 To Ylva i Abstract Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved.Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. 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 fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed.A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault f i from another fault f j ?". Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance.As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance a...

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A survey on diagnostic methods for automotive engines

Cited by 52 publications

References 142 publications

System Identification of an Engine-load Setup Using Grey-box Model

System Identification of an Engine-load Setup Using Grey-box Model

Automotive engine FDI by application of an automated model-based and data-driven design methodology

Diagnosability performance analysis of models and fault detectors

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