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

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

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

Accelerated artificial neural networks on FPGA for fault detection in automotive systems

Modern vehicles are complex distributed systems with critical real-time electronic controls that have progressively replaced their mechanical/hydraulic counterparts, for performance and cost benefits. The harsh and varying vehicular environment can induce multiple errors in the computational/communication path, with temporary or permanent effects, thus demanding the use of fault-tolerant schemes. Constraints in location, weight, and cost prevent the use of physical redundancy for critical systems in many cases, such as within an internal combustion engine. Alternatively, algorithmic techniques like artificial neural networks (ANNs) can be used to detect errors and apply corrective measures in computation. Though adaptability of ANNs presents advantages for fault-detection and fault-tolerance measures for critical sensors, implementation on automotive grade processors may not serve required hard deadlines and accuracy simultaneously. In this work, we present an ANN-based fault-tolerance system based on hybrid FPGAs and evaluate it using a diesel engine case study. We show that the hybrid platform outperforms an optimised software implementation on an automotive grade ARM Cortex M4 processor in terms of latency and power consumption, also providing better consolidation

The application of black box models to combustion processes in the internal combustion engine

The internal combustion engine has been under considerable pressure during the last few years. The publics growing sensitivity for emissions and resource wastage have led to increasingly stringent legislation. Engine manufacturers need to invest significant monetary funds and engineering resources in order to meet the designated regulations. In recent years, reductions in emissions and fuel consumption could be achieved with advanced engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines (VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic reduction (SCR) implementations. These approaches are characterised by a highly non-linear behaviour with an increasing demand for close-loop control. In consequence, successful controller design becomes an important part of meeting legislation requirements and acceptable standards. At the same time, the close-loop control requires additional monitoring information and, especially in the field of combustion control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder pressure detection enable the feedback of combustion conditions. However, high maintenance costs and reliability issues currently cancel this method out for mass-production vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been presented over the last few decades. The methodical restrictions of these approaches are proving problematic. Hence, this work presents a method utilising artificial neural networks for the prediction of combustion-related engine parameters. The application of networks for the prediction of parameters such as emission formations of NOx and Particulate Matters will be shown initially. This thesis shows the importance of correct training and validation data choice together with a comprehensive network input set. In addition, an application of an efficient and accurate plant model as a support tool for an engine fuel-path controller is presented together with an efficient test data generation method. From these findings, an artificial neural network structure is developed for the prediction of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable information about the combustion efficiency and quality. This work presents a structure that can predict these parameters from other more simple measurable variables within the engine auxiliaries. The structure is tested on data generated from a GT-Power simulation model and with a Caterpillar C6.6 heavy-duty diesel engine