Vicente Macián

Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis

Abstract Wear has important, negative effects on the functioning of engine parts. Additionally, this situation is very difficult to evaluate accurately in oil analysis for engine condition monitoring. Original Equipment Manufacturers (OEM), lubricant suppliers and oil analysis laboratories provide specific guidelines for wear metal concentrations. These limits provide good general guidelines for interpreting oil analysis data, but do not take into account common factors that influence the concentration of wear debris and contaminants in an oil sample. These factors involve oil consumption, fresh oil additions, etc., and particular features such as engine age, type of service, environmental conditions, etc. In this paper, an analytical approach to enable a more accurate wear determination from engine oil samples is developed. The above factors are taken into account and an improved maintenance program for internal combustion engines based on oil analysis is developed.

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Abstract: Antipollution legislation in automotive internal combustion engines requires active control and prediction of pollutant formation and emissions. Predictive emission models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states.

Exhaust pressure pulsation observation from turbocharger instantaneous speed measurement

In internal combustion engines, instantaneous exhaust pressure measurements are difficult to perform in a production environment. The high temperature of the exhaust manifold and its pulsating character make its application to exhaust gas recirculation control algorithms impossible. In this paper an alternative method for estimating the exhaust pressure pulsation is presented. A numerical model is built which enables the exhaust pressure pulses to be predicted from instantaneous turbocharger speed measurements. Although the model is data based, a theoretical description of the process is also provided. This combined approach makes it possible to export the model for different engine operating points. Also, compressor contribution in the turbocharger speed pulsation is discussed extensively. The compressor contribution is initially neglected, and effects of this simplified approach are analysed.

Proposal of an FTIR Methodology to Monitor Oxidation Level in Used Engine Oils: Effects of Thermal Degradation and Fuel Dilution

This article describes a procedure, based on ASTM standards D7214 and E2412, that has been defined to improve quantification of oil oxidation in used engine oils. Taking into account typical problems that can be found in this type of sample, including thermal oxidation and fuel dilution, Fourier transform infrared (FTIR) spectra were analyzed also considering the effect of the oil formulation. Two zones were considered inside the typical wavenumber range for quantification of oxidation, where those problems can be detected and assessed more easily: zone A between 1725 and 1650 cm−1, where the main oxidation products, such as aldehydes, carboxylic acids, and ketones, occur due to thermal degradation of the oil; and zone B between 1770 and 1725 cm−1, where esters due to potential biodiesel dilution problems are detected.

DFT-based controller for fuel injection unevenness correction in turbocharged diesel engines

Although combustion failure diagnosis techniques have been widely developed over the last few years, real-time correction of fuel injection failures, such as drift, are still deficient. In this paper, a controller for the correction of fuel injection failures is presented; the aim of the algorithm is to ensure that the same quantity of fuel is injected in each one of the cylinders. The controller is based on a linear model that relates the low-frequency region of the Fourier transform of a dynamic engine signal and fuel injection unevenness. Model inversion is used as an injection failure observer, and discrepancies in the injected fuel mass in each cylinder can be estimated, even in the case of multiple and simultaneous failures. This observer is used for closing the loop and performing the control action via an integral controller. Theoretical bases are given for the controller, and the stability and settling error are related to the error of the linear engine model assumed. This technique can be used for different engine signals, like crankshaft speed, exhaust manifold pulsation, and turbocharger instantaneous speed. Experimental results obtained on a diesel turbocharged engine, where the turbocharger instantaneous speed was used as input information of the controller, are presented proving the performance of the fuel quantity control algorithm

Fuzzy Diagnosis Module Based on Interval Fuzzy Logic: Oil Analysis Application

This paper presents the basic characteristics of a prototype fuzzy expert system for condition monitoring applications, in particular, oil analysis in Diesel engines. The system allows for reasoning under absent or imprecise measurements, providing with an interval-valued diagnostic of the suspected severity of a particular fault. A set of so-called metarules complements the basic fault dictionary for fine tuning, allowing extra functionality. The requirements and basic knowledge base for an oil analysis application are also outlined as an example.

Wear Rate Determination for IC Engine Condition Monitoring Results Obtained in an Urban Transport Fleet

This paper is structured into two different parts: Firstly, it describes a methodology to evaluate wear conditions in internal combustion engines in order to go beyond the classical evaluation based on specified wear concentration limits provided by engine manufacturers or commercial oil laboratories. The proposed methodology uses spectrometric wear debris measurement data and typical maintenance data to obtain a more representative parameter of wear condition, defined as compensated wear rate, that takes into account particular engine operating conditions affecting wear concentration measurements. Later, an evaluation of this compensated wear rate is carried out using statistical criteria and considering individual engine characteristics such as engine age, type of service, engine metallurgy, environmental conditions of work etc. Secondly, results obtained from an investigation carried out on oil samples from engines of an urban transport fleet are presented, confirming the value of compensated wear rate as a more representative parameter of engine wear behaviour during the oil use period than absolute concentration values.

A view on the internal consistency of linear source identification for I.C. engine exhaust noise prediction

Abstract Considerable efforts have been devoted to the development of predictive models that, from a certain set of data related to an engine, and making use of an adequate representation of the effect of the silencing elements, provide an estimate of the exhaust noise emitted. Such models should allow for the consideration of the engine and its interaction with the exhaust system. This is properly achieved by gas-dynamic models, which are becoming the standard, but linear models solved in the frequency domain and representing the engine as a linear time-invariant source may still play a role in exhaust system design, as the engine is treated as a black box. Such a representation is very attractive for engine manufacturers, since it gives the possibility to provide data on the engine without any possibility to trace back to its real characteristics. In order to provide additional criteria for the suitability of the application of a linear time-invariant representation to an engine exhaust, in this paper a multi-load method has been used to extract source characteristics from gas-dynamic simulation results. The details of the method, in which the resulting over-determined system is solved by fitting the values of the source parameters in a least-squares sense, are described, and different approaches are used in order to check the internal consistency of the source representation: the identification of pressure and velocity sources, and the application of the least-squares criterion to the modulus or to the real and imaginary parts separately. In particular, eight different determinations of the source impedance are obtained and, considering the application of the formalism to an engine exhaust, the differences observed provide a suitable criterion for the evaluation of the suitability of the representation and of the particular set of loads chosen.

Data-Driven Design of Takagi-Sugeno Fuzzy Systems for Predicting NOx Emissions

New emission abatement technologies for the internal combustion engine, like selective catalyst systems or diesel particulate filters, need of accurate, predictive emission models. These models are not only used in the system calibration phase, but can be integrated for the engine control and on-board diagnosis tasks. In this paper, we are investigating a data-driven design of prediction models for NOx emissions with the help of (regression-based) Takagi-Sugeno fuzzy systems, which are compared with analytical physical-oriented models in terms of practicability and predictive accuracy based on high-dimensional engine data recorded during steady-state and dynamic engine states. For training the fuzzy systems from data, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically finds an appropriate number of rules by an incremental and evolving clustering approach and estimates the consequent parameters with the local learning approach in order to optimize the weighted least squares functional.

Methodology of fault detection in internal combustion engines through the analysis of rolling block oscillation

This paper presents a non-intrusive fault detection technique, based on the analysis of rolling block oscillations, assuming that the engine behaves as a rigid body mounted on elastic supports. Engine diagnosis is feasible with a single accelerometer, which constitutes an attractive procedure for maintenance engineers. The proposed method is illustrated with modelled and measured results obtained with two different automotive engines. The engine diagnosis is performed using a particular normalisation of the Fast Fourier Transform. The procedure leads to identify multiple sub-harmonics of the firing frequency which allow a reliable diagnosis.