Misfire Detection for Vehicles with Dual Mass Flywheel (DMF) Based on Reconstructed Engine Torque

“…The recurrence formula of elastic forces of the spring coils can be derived from equation (3). However, when the external torque is too small to overcome the friction force, the spring coil stays stick.…”

Section: Nonlinearity Of the Stiffnessmentioning

confidence: 99%

“…A applicable model of DMF is significant to predict the dynamic behaviors of power train. Walter et al [2][3][4] and Lei et al 5 have simplified a DMF as a 2-degree-of-freedom system, which is a linear mass-spring-damper model. Using this model, the torsional vibration characteristics of the power train fitted with the DMF are predicted.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear dynamical model of an automotive dual mass flywheel

Chen

Zeng

Jiang

2015

Advances in Mechanical Engineering

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The hysteresis, stick-slip, and rotational speed-dependent characteristics in a basic dual mass flywheel are obtained from a static and a dynamic experiments. Based on the experimental results, a nonlinear model of the transferred torque in this dual mass flywheel is developed, with the overlying form of nonlinear elastic torque and frictional torque. The nonlinearities of stiffness are investigated, deriving a nonlinear model to describe the rotational speed-dependent stiffness. In addition, Bouc-Wen model is used to model the hysteretic frictional torque. Thus, the nonlinear 2-degree-of-freedom system of this dual mass flywheel is set up. Then, the Levenberg-Marquardt method is adopted for the parameter estimation of the frictional torque. Finally, taking the nonlinear stiffness in this model into account, the parameters of BoucWen model are estimated based on the dynamic test data.

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“…One approach is torque estimation and has been studied in, e.g, (Connolly and Rizzoni, 1994;Kiencke, 1999;Walter et al, 2007). The purpose is to recreate the torque generated by the cylinders to detect torque drops due to misfire.…”

Section: Approaches To Engine Misfire Detectionmentioning

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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Misfire Detection for Vehicles with Dual Mass Flywheel (DMF) Based on Reconstructed Engine Torque

Cited by 15 publications

References 5 publications

Modeling and torque estimation of an automotive Dual Mass Flywheel

Modeling and torque estimation of an automotive Dual Mass Flywheel

Nonlinear dynamical model of an automotive dual mass flywheel

Diagnosability performance analysis of models and fault detectors

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