Detection of misfire and knock in spark ignition engines by wavelet transform of engine block vibration signals

Chang, Jinseok; Kim, Manshik; Min, Kyoungdoug

doi:10.1088/0957-0233/13/7/319

Cited by 59 publications

(32 citation statements)

References 14 publications

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“…Regarding noise radiation through the engine structure, previous investigations have shown different propagation patterns of the acoustic energy [34,35,36]. This acoustic response is highly non-linear and time dependent and it is extremely influenced by the engine block design.…”

Section: Introductionmentioning

confidence: 99%

Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine

Broatch

Margot

Novella

et al. 2016

Energy

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ElsevierBroatch Jacobi, JA.; Margot, X.; Novella Rosa, R.; Gómez-Soriano, J. (2016). Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine. Energy. 107:612-624. AbstractIn the last decade, different advanced combustion concepts based on generating totally or partially premixed conditions have been investigated in CI engines with the aim of achieving lower NOx and soot emissions. Most of the drawbacks inherent to this type of combustions, such as the combustion phasing control or combustion stability, can be mitigated by combining the PPC concept fueled by gasoline and a small 2-stroke HSDI engine. However, combustion noise issue remains unsolved while it is a critical aspect due to its strong influence in the customer purchasing decision and compliance of more stringent regulations. In this work, an analysis of the combustion noise generated by PPC combustion concept is performed in order to identify the most influential parameters and to define key paths for controlling the noise level. In addition, 3D CFD simulations have been performed to further understand the combustion noise generation mechanisms. Results evidence how the strong impact of the maximum pressure time-derivative achieved during combustion process renders all other sources of noise generation irrelevant. The trade-off between combustion noise and combustion efficiency of this PPC concept has been confirmed, while the intrinsic relation between such parameters and the engine efficiency has been also evaluated.

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Section: Introductionmentioning

confidence: 99%

Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine

Broatch

Margot

Novella

et al. 2016

Energy

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“…Examples of approaches are to directly monitor the quality of the combustion using ion current sensors (Auzins et al, 1995;Fan et al, 2014;Lundström and Schagerberg, 2001), to measure engine vibration using an accelerometer mounted on the engine block (Chang et al, 2002;Sharma et al, 2014;Sugumaran et al, 2010), or to detect temperature variations in the exhaust manifold (Tamura et al, 2011). These methods usually require additional sensors that are not generally available in the vehicle.…”

Section: Approaches To Engine Misfire Detectionmentioning

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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“…The acceleration signals contain more information about high frequency components while the torsional vibration can give more precise information about low frequency components. Some research [1][2][3][4][5][6][7] has looked at using translational acceleration signal measurement for the misfire diagnosis. This approach involves recording vibrations at a position either on the engine block or on the cylinder head and requires the design of an appropriate inverse filter, usually in the form of a parametric model.…”

Section: Introductionmentioning

confidence: 99%

Improved automated diagnosis of misfire in internal combustion engines based on simulation models

Chen

Randall

2015

Mechanical Systems and Signal Processing

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In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

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Detection of misfire and knock in spark ignition engines by wavelet transform of engine block vibration signals

Cited by 59 publications

References 14 publications

Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine

Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine

Diagnosability performance analysis of models and fault detectors

Improved automated diagnosis of misfire in internal combustion engines based on simulation models

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