MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH

Misfire is another type of abnormal combustion. When engine misfires, cylinder (or cylinders) is not producing its normal amount of power. Engine misfire also has negative effects on engine exhaust emissions such as HC, CO, and NOx. Engine misfire should be detected and eliminated. Normal combustion and misfire in the cylinder (if any) generates vibrations in the engine block. The vibration characters due to misfire are unique for a particular cylinder. This can be diagnosed by processing the vibration signals acquired from the engine cylinder block using a piezoelectric accelerometer. The obtained signals were decoded using statistical parameters, like, Kurtosis, standard deviation, mean, median, etc. Misfire identification algorithms such as AdaBoost, LogitBoost, MultiClass Classifier, and J48 were used as tools for feature selection and classification. The signals were trained and tested by the selected classifiers. The classification accuracy of selected classifiers were compared and presented in this paper. MultiClass Classifier was found to be performing better with selected statistical features compared to other classifiers

IDENTIFICATION OF HEAT RELEASE SHAPES AND COMBUSTION CONTROL OF AN LTC ENGINE

Low Temperature Combustion (LTC) regimes have gained attention in internal combustion engines since they deliver low nitrogen oxides (NOx) and soot emissions with higher thermal efficiency and better combustion efficiency, compared to conventional combustion regimes. However, the operating region of these high-efficiency combustion regimes is limited as it is prone to knocking and high in-cylinder pressure rise rate outside the engine safe zone. By allowing multi-regime operation, high-efficiency region of the engine is extended. To control these complex engines, understanding and identification of heat release rate shapes is essential. Experimental data collected from a 2 liter 4 cylinder LTC engine with in-cylinder pressure measurements, is used in this study to calculate Heat Release Rate (HRR). Fractions of early and late heat release are calculated from HRR as a ratio of cumulative heat release in the early or late window to the total energy of the fuel injected into the cylinder. Three specific HRR patterns and two transition zones are identified. A rule based algorithm is developed to classify these patterns as a function of fraction of early and late heat release percentages. Combustion parameters evaluated also showed evidence on characteristics of classification. Supervised and unsupervised machine learning approaches are also evaluated to classify the HRR shapes. Supervised learning method ( Decision Tree)is studied to develop an automatic classifier based on the control inputs to the engine. In addition, supervised learning method (Convolutional Neural Network (CNN)) and unsupervised learning method (k-means clustering) are studied to develop an automatic classifier based on HRR trace obtained from the engine. The unsupervised learning approach wasn\u27t successful in classification as the arrived k-means centroids didn\u27t clearly represent a particular combustion regime. Supervised learning techniques, CNN method is found with a classifier accuracy of 70% for identifying heat release shapes and Decision Tree with the accuracy of 74.5% as a function of control inputs. On rule based classified traces with the use of principle component analysis (PCA) and linear regression, heat release rate classifiers are built as a function of engine input parameters including, Engine speed, Start of injection (SOI), Fuel quantity (FQ) and Premixed ratio (PR). The results are then used to build a linear parameter varying (LPV) model as a function of the modelled combustion classifiers by using the least square support vector machine (LS-SVM) approach. LPV model could predict CA50(Combustion phasing), IMEP (indicated mean effective pressure) and MPRR (maximum pressure rise rate) with a RMSE of 0.4 CAD, 16.6 kPa and 0.4 bar/CAD respectively. The designed LPV model is then incorporated in a model predictive control (MPC) platform to adjust CA50, IMEP and MPRR. The results show the designed LTC engine controller could track CA50 and IMEP with average error of 1.2 CAD and 6.2 kPa while limiting MPRR to 6 bar/CAD. The controller uses three engine inputs including, SOI, PR and FQ as manipulated variables, that are optimally changed to control the LTC engine

Internal Combustion Engines

This book on internal combustion engines brings out few chapters on the research activities through the wide range of current engine issues. The first section groups combustion-related papers including all research areas from fuel delivery to exhaust emission phenomena. The second one deals with various problems on engine design, modeling, manufacturing, control and testing. Such structure should improve legibility of the book and helps to integrate all singular chapters as a logical whole

Monitoring of the piston ring-pack and cylinder liner interface in diesel engines through acoustic emission measurements

Investigation of novel condition monitoring systems for diesel engines has received much recent attention due to the increasing demands placed upon engine components and the limitations of conventional techniques. This thesis documents experimental research conducted to assess the monitoring capabilities of Acoustic Emission (AE) analysis. In particular it focuses on the possibility of monitoring the piston ring-pack and cylinder liner interface, a critical engine sub-system for which there are currently few practical monitoring options. A series of experiments were performed on large, two-stroke and small, four-stroke diesel engines. Tests under normal operating conditions developed a detailed understanding of typical AE generation in terms of both the source mechanisms and the characteristics of the resulting activity. This was supplemented by specific tests to investigate possible AE generation at the ring-pack/liner interface. For instance, for the small engines measures were taken to remove known AE sources in order to accentuate any activity originating at the interface whilst for the large engines the interfacial conditions were purposely deteriorated through the removal of the lubricating oil supply to one cylinder. Interpretation of the results was based mainly upon comparisons with published work encompassing both the expected ring-pack behaviour and AE generation from tribological processes. This provided a strong indication that the source of the ring-pack/liner AE activity was the boundary frictional losses. The ability to monitor this process may be of significant benefit to engine operators as it enhances the diagnostic information currently available and may be incorporated into predictive maintenance strategies. A further diagnostic technique considered was the possibility of using AE parameters combined with information of crankshaft speed fluctuations to evaluate engine balance and identify underperforming cylinders.EU Competitive and Sustainable Growth Programme, Project no: GRD2-2001-5001

Application of Rough Classification of Multi-objective Extension Group Decision-making under Uncertainty

On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making. Keywords: extension group decision-making; matter-element analysis; extension association; rough set; attribution reductio

New Trends on the Combustion Processes in Spark Ignition Engines

This Special Issue on "New Trends on the Combustion Processes in Spark Ignition Engines" contains nine papers on new developments on Internal Combustion (IC) engines aiming to enhance their efficiency, leading to the reduction of fossil CO2 and other gaseous pollutants. It is divided into two parts. In the initial part, the focus in on fuels, with four papers discussing the use of biofuels and other alternative fuels that can be used in different types of IC Engines. Additionally, conventional fuels are tested in order to evaluate their optimal use in new downsizing high-boost engines. A revision paper on alternative fuels is also included. The second part involves the study and improvement of engine combustion diagnostics as well as the presentation of an alternative type of propulsion system

Investigation of Combustion Phenomena in a Single-Cylinder Spark-Ignited Natural Gas Engine with Optical Access

More demanding efficiency and emissions standards for internal combustion (IC) engines require novel combustion strategies, alternative fuels, and improved after-treatment systems. However, their development depends on improved understanding of in-cylinder processes. For example, the lower efficiency of conventional spark-ignited (SI) natural-gas (NG) engines reduces their utilization in the transportation sector. Single-cylinder optical-access research engines allow the use of non-intrusive visualization techniques that study in-cylinder flow, fuel-oxidizer mixing, and combustion and emissions phenomena under conditions representative of production engines. These visualization techniques can provide qualitative and quantitative answers to fundamental combustion-phenomena questions such as the effects of engine design, operating conditions, fuel composition, fuel delivery strategy, and ignition techniques.;The thesis is divided in two main parts. The first part focuses on the setup of a single-cylinder research engine with optical access including the design of its control system and the acquisition of in-cylinder pressure data and high-speed combustion images. The second part focuses on measurements of the turbulent flame speed using the high-speed combustion images. Crank-angle-resolved images of methane combustion were taken with a high-speed CMOS camera at a rate of 15,000 Hz. The optical engine was operated in a skip-firing mode (one fired cycle followed by 5 motored cycles) at 900 RPM and a load of 5.93 bar IMEP. The images show that flow turbulence and flame stretch resulted in flame velocities several order of magnitude higher compared to the laminar flame velocity. In addition, both in-cylinder pressure and optical data were used to determine the cycle-to cycle variability of the combustion phenomena