Combined fault detection and classification of internal combustion engine using neural network

Different faults in internal combustion engines leads to excessive fuel consumption, pollution, acoustic emission and wear of engine components. Detection of fault is also difficult for maintenance technicians due to broad range of faults and combination of the faults. In this research the faults due to malfunction of manifold absolute pressure, knock sensor and misfire are detected and classified by analyzing vibration signals. The vibration signals acquired from engine block were preprocessed by wavelet analysis, and signal energy is considered as a distinguishing property to classify these faults by a Multi-Layer Perceptron Neural Network (MLPNN). The designed MLPNN can classify these faults with almost 100 % efficiency

Modeling and experimental analysis of exhaust gas temperature and misfire in a converted-diesel homogeneous charge compression ignittion engine fuelled with ethanol

Homogeneous charge compression ignition (HCCI) and the exploitation of ethanol as an alternative fuel is one way to explore new frontiers of internal combustion engines with an objective towards maintaining its sustainability. Here, a 0.3 liter singlecylinder direct-injection diesel engine was converted to operate on the alternative mode with the inclusion of ethanol fuelling and intake air preheating systems. The main HCCI engines parameters such as indicated mean effective pressure, maximum in-cylinder pressure, heat release, in-cylinder temperature and combustion parameters, start of combustion, 50% of mass fuel burnt (CA50) and burn duration were acquired for 100 operating conditions. They were used to study the effect of varying input parameters such as equivalence ratio and intake air temperature on exhaust gas emission, temperature and ethanol combustion, experimentally and numerically. The study primarily focused on HCCI exhaust gas temperature and understanding and detecting misfire in an ethanol fuelled HCCI engine, thus highlighting the advantages and drawbacks of using ethanol fuelled HCCI. The analysis of experimental data was used to understand how misfire affects HCCI engine operation. A model-based misfire detection technique was developed for HCCI engines and the validity of the obtained model was then verified with experimental data for a wide range of misfire and normal operating conditions. The misfire detection is computationally efficient and it can be readily used to detect misfire in HCCI engine. The results of the misfire detection model are very promising from the viewpoints of further controlling and improving combustion in HCCI engines

APPLICATION OF SENSOR FUSION FOR SI ENGINE DIAGNOSTICS AND COMBUSTION FEEDBACK

Shifting consumer mindsets and evolving government norms are forcing automotive manufacturers the world over to improve vehicle performance and also reduce greenhouse gas emissions. A critical aspect of achieving future fuel economy and emission targets is improved powertrain control and diagnostics. This study focuses on using a sensor fusion based approach to improving control and diagnostics in a gasoline engine. A four cylinder turbocharged engine was instrumented with a suite of sensors including ion sensors, exhaust pressure sensors, crank position sensors and accelerometers. The diagnostic potential of these sensors was studied in detail. The ability of these sensors to detect knock, misfires and also correlate with pressure and combustion metrics was also evaluated. Lastly a neural network based approach to combine individual sensor signal information was developed. The neural network was used to estimate mean effective pressure and location of fifty percent mass fraction fuel burn. Additionally, the influence of various neural network architectures was studied. Results showed that under pseudo transient conditions a recursive neural network could use information from the low cost sensors to estimate mean effective pressure within an error of 0.1bar and combustion phasing within 2.5 crank-angle degrees

Review of sensing methodologies for estimation of combustion metrics

For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Air-fuel Ratio Estimation Along Diesel Engine Transient Operation Using In-cylinder Pressure

Abstract The increasing competition among automotive OEMs together with the worsening of the environmental pollution has lead to the development of complex engine systems. Innovative control strategies are needed to simplify and improve the Engine Management System (EMS), moving towards energy saving and complying with the restrictions on emissions standards. In this scenario the application of methodologies based on the in-cylinder pressure measurement finds widespread applications. Indeed, the in-cylinder pressure signal provides direct in-cylinder information with a high dynamical potentiality that is fundamental for the control and diagnosis of the combustion process. Furthermore, the in-cylinder pressure measurement may also allow reducing the number of existing sensors on-board, thus lowering the equipment costs and the engine wiring complexity. The paper focuses on the estimation of the Air-Fuel ratio from the in-cylinder pressure signal. The methodology is based on the analysis of the statistical moments of the pressure cycle and was already presented by the authors and applied to a set of steady state engine operation conditions. In this paper the technique has been enhanced in order to be applied under the more critical engine transient operation. The results achieved show a satisfactory accuracy in predicting the Air-Fuel ratio during engine transients performed at the engine test bench on a Common-Rail turbocharged Diesel engine

MODELING AND EXPERIMENTAL SETUP OF AN HCCI ENGINE

For the past three decades the automotive industry is facing two main conflicting challenges to improve fuel economy and meet emissions standards. This has driven the engineers and researchers around the world to develop engines and powertrain which can meet these two daunting challenges. Focusing on the internal combustion engines there are very few options to enhance their performance beyond the current standards without increasing the price considerably. The Homogeneous Charge Compression Ignition (HCCI) engine technology is one of the combustion techniques which has the potential to partially meet the current critical challenges including CAFE standards and stringent EPA emissions standards. HCCI works on very lean mixtures compared to current SI engines, resulting in very low combustion temperatures and ultra-low NOx emissions. These engines when controlled accurately result in ultra-low soot formation. On the other hand HCCI engines face a problem of high unburnt hydrocarbon and carbon monoxide emissions. This technology also faces acute combustion controls problem, which if not dealt properly with yields highly unfavorable operating conditions and exhaust emissions. This thesis contains two main parts. One part deals in developing an HCCI experimental setup and the other focusses on developing a grey box modelling technique to control HCCI exhaust gas emissions. The experimental part gives the complete details on modification made on the stock engine to run in HCCI mode. This part also comprises details and specifications of all the sensors, actuators and other auxiliary parts attached to the conventional SI engine in order to run and monitor the engine in SI mode and future SI-HCCI mode switching studies. In the latter part around 600 data points from two different HCCI setups for two different engines are studied. A grey-box model for emission prediction is developed. The grey box model is trained with the use of 75% data and the remaining data is used for validation purpose. An average of 70% increase in accuracy for predicting engine performance is found while using the grey-box over an empirical (black box) model during this study. The grey-box model provides a solution for the difficulty faced for real time control of an HCCI engine. The grey-box model in this thesis is the first study in literature to develop a control oriented model for predicting HCCI engine emissions for control

Application of artificial neural network to classify fuel octane number using essential engine operating parameters

Real-time fuel octane number classification is essential to ensure that spark ignition engines operation are free of knock at best combustion efficiency. Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. Presently, there is no research which takes into account the fuel tendency to knock in real-time engine operation. This research proposed the use of on-board detection of fuel octane number by implementing a simple methodology and use of a non-intrusive sensor. In the experiment, the engine was operated at different speeds, load, spark advance and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. The RON classification procedure was investigated using regression analysis as a classic pattern recognition methodology and artificial neural network (ANN) by executing combustion properties derived from in-cylinder pressure signal and engine rotational speed signal. The in-cylinder pressure analysis illustrated the knock-free, light-knock and heavy-knock regions for all engine operating points. The results showed a special pattern for each fuel RON using peak in-cylinder pressure, maximum rate of pressure rise and maximum amplitude of pressure oscillations. Besides, there is a requirement for pre-defined threshold or formula to restrict the implementation of these parameters for on-board fuel identification. The ANN model efficiency with pressure signal as network input had the highest accuracy for all spark advance timing. However, the ANN model with rotational speed signal input only had the ability to identify the fuel octane number after a specific advance timing which was detected at the beginning of noisy combustion due to knock. The confusion matrix for the ANN with speed signal input had increased from 68.1% to 100% by advancing the ignition from -10° to -30° before top dead centre. The results established the ability of rotational speed signal for fuel octane classification using the relation between knock and RON. The implication is that all the production spark ignition engines are equipped with engine speed sensor, thus, this technique can be applied to all engines with any number of cylinders