Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC) Valves Used in Diesel Engines

In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC) valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS) type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves

AUTOMATED DIESEL ENGINE CONDITION & PERFORMANCE MONITORING & THE APPLICATION OF NEURAL NETWORKS TO FAULT DIAGNOSIS

The overall aim of this research was to design, configure and validate a system which was capable of on-line performance monitoring and fault diagnosis of a diesel engine. This thesis details the development and evaluation of a comprehensive engine test facility and automated engine performance monitoring package. Results of a diesel engine fault study were used to ascertain commonly occurring faults and their realistic severities are discussed. The research shows how computer simulation and rig testing can be applied to validate the effects of faults on engine performance and quantify fault severities. A substantial amount of engine test work has been conducted to investigate the effects of various faults on high speed diesel engine performance. A detailed analysis of the engine test data has led to the development of explicit fault-symptom relationships and the identification of key sensors that may be fitted to a diesel engine for diagnostic purposes. The application of a neural network based approach to diesel engine fault diagnosis has been investigated. This work has included an assessment of neural network performance at engine torques and speeds where it was not trained, noisy engine data, faulty sensor data, varying fault severities and novel faults which were similar to those which the network had been trained on. The work has shown that diagnosis using raw neural network outputs under operational conditions would be inadequate. To overcome these inadequacies a new technique using an on-line diagnostic database incorporating 'weight adjusting' and 'confidence factor' algorithms has been developed and validated. The results show a neural network combined with an on-line diagnostic database can be successfully used for practical diesel engine fault diagnosis to offer a realistic alternative to current fault diagnosis techniques.The Ministry Of Defenc

Condition Monitoring and Fault Diagnosis of a Marine Diesel Engine with Machine Learning Techniques

A marine engine room is a complex system in which many different subsystems are interacting with each other. At the center of this system is the main diesel engine which produces the propulsion force. Many other components such as compressed air, cooling, heating, lubricating oil, fuel, and pumping systems act as auxiliary machines to the main engine. Automation of many functions in the engine room is starting to play an important role in new generation ships to provide better control using sensors monitoring the engine and its environment. Sensors exist in the current generation ships, but engineers evaluate the sensor data for the presence of any problems. Maintenance actions are taken based on these manual analyses or regular maintenance is carried out at times determined by manufacturers, whether such actions are needed or not. With machine learning, it is possible to develop an algorithm using past evaluations made by engineers. Recent studies show that highly accurate results can be obtained using machine learning methods when there is sufficient data. In this study, we develop new learning-based algorithms and evaluate them on data obtained from a realistic ship engine room simulator. Data for a predetermined set of parameters of a high-power diesel engine were collected and analyzed for their role in a set of fault situations. These fault conditions and the associated sensor data are used to train a set of classifiers achieving fault detection up to 99% accuracy. These are promising results in preventing future damage to the engine or its supporting components by predicting failures before they occur

An investigation into frequency resolution estimation model for impact signal analysis by using Hilbert spectrum and condition classification for marine diesel engine

In this paper, frequency resolution determination method is investigated according to Hilbert spectrum performance for impact signal analysis. A new constructed performance estimation model for the best frequency resolution is put forward in this research for the impact signal pattern recognition. Different parameters in the time-frequency distribution by using Hilbert spectrum are considered in this estimation model for the best frequency resolution determination. To verify the effectiveness of this estimation model, numerical simulation is used for Hilbert spectrum construction analysis. At the same time, different marine diesel engine working condition signals analysis are also used to illustrate the methodology developed in this research and verify the effectiveness. It can be concluded that this method can contribute the development for impact signal analysis by using Hilbert spectrum

A modelling approach for predicting marine engines shaft dynamics

For making decisions on maintenance and operations of ship systems in a timely and cost effective way, intelligent approaches for continuously assessing the critical ship systems condition are required. This study aims to provide a framework for large marine two-stroke diesel engines performance assessment, by mapping the relationship of specific malfunctioning engine conditions on the Instantaneous Crankshaft Torque (ICT). This is accomplished by the development of a thermodynamics model, which is coupled with a lumped mass crankshaft dynamics model, in order to predict the engine shaft dynamics and torsional response. Subsequently, by employing the coupled engine models, a number of case studies are simulated for investigating the influence on the engine ICT, which include: (a) change in the Start of Injection (SOI), (b) change in the Rate of Heat Release (RHR), (c) change in the scavenge air pressure, and (d) leaking exhaust valve. By investigating the predicted ICT from the coupled model in both the time and frequency domains, distinct frequencies are identified, which correspond to specific engine malfunctioning conditions. Based on the derived results, these engine malfunctioning conditions are mapped with the frequencies most affected in the engine’s instantaneous torque, which demonstrate the usefulness of implementing the the ICT measurement for diagnostic purposes

Marine diesel engines operating cycle simulation for diagnostics issues

The ongoing monitoring of marine diesel engines helps to detect the deviations of its parameters early and prevent major failures. But the experimental diagnostics data are generally limited, so frequently, it isn’t possible to get all the necessary information to make a clear decision. The mathematical simulation could be used to clarify the experimental data and to provide a deeper understanding of engine conditions. In this paper, the MAN 6L80MCE marine diesel engine of “Father S” bulk carrier diagnostics issues are considered. The diagnostics data were collected with DEPAS Handy equipment and present the information about indicated processes by every engine cylinder. The on-line resource Blitz-PRO was used for the simulation of the engine operation and helped to prove that the variation in exhaust valve’s closing timing is responsible for the observed compression pressure difference, while the irregularity in fuel injection causes the considerable difference in the maximum pressure

A Comparison Between Digital-Twin Based Methodologies for Predictive Maintenance of Marine Diesel Engine

An efficient operation of marine diesel engines, onboard ships, requires advanced monitoring and diagnostic techniques for early detection of faults and degradation in the propulsion or power generation system. This complex problem has been recently approached by digital-twin-based fault detection models. In this paper, we report on two methods for fault analysis on marine diesel engines exploiting (i) an Artificial Neural Network (ANN) combined with machine learning tools and (ii) a digital twin simulation model combined with a parameter estimator tool. In both cases, a digital twin model of the engine has been used for the generation of synthetic data, but in different simulation environments. These methodologies are applied to two distinct case studies, and their outcomes are discussed, focusing on the pros and cons. A proposal for a method combining the benefits of both is presented