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 Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

Fault Diagnosis for Multi-energy Flows of Energy Internet: Framework and Prospects

Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed.National Natural Science Foundation of China 61703345National Natural Science Foundation of China 61472328National Natural Science Foundation of China 5160714

THE INVESTIGATION INTO THE CONDITION MONITORING OF TRIBOLOGICAL BEHAVIOUR BETWEEN PISTON RING AND CYLINDER LINER USING ACOUSTIC EMISSIONS

To improve engine operational performance and reliability, this study focuses on the investigation into the behaviour of tribological conjunction between the ring - liner based on a comprehensive analysis of non-intrusive acoustic emission (AE) measurement. Particularly, the study will provide more knowledge of using AE for online monitoring and diagnosing the performances of the conjunction. To fulfil this study, it integrates analytical predictions of the theoretical modelling for the AE generation mechanism with extensive experimental evaluations. Moreover, effective signal processing techniques are implemented with a combination of the model based AE predictions to extract the weak and nonstationary AE contents that correlate more with the tribological behaviour. Based on conventional tribological models, tribological AE is modelled to be due to two main dynamic effects: asperity-asperity collision (AAC) and fluid-asperity interaction (FAI), which allows measured AE signals from the tribological conjunction to be explained under different scenarios, especially under abnormal behaviours. FAI induced AE is more correlated with lubricants and velocity. It presents mainly in the middle of engine strokes but is much weaker and severely interfered with AEs from not only valve landings, combustion and fuel injection shocks but also the effect of considerable AACs due to direct contacts and solid particles in oils. To extract weak AEs for accurately diagnosing the tribological behaviours, wavelet transform analysis is applied to AE signals with three novel schemes: 1) hard threshold based wavelet coefficients selection in which the threshold value and wavelet analysis parameters are determined using a modified velocity of piston motion which has high dependence on the AE characteristics predicted by the two models; 2) Adaptive threshold wavelet coefficients selection in which the threshold is gradually updated to minimise the distance between the AE envelopes and the predicted dependence; and 3) wavelet packet transform (WPT) analysis is carried out by an optimised Daubechies wavelet through a novel approach based on minimising the time and frequency overlaps in WPT spectrum. Based on these optimal analyses, the local envelope amplitude (LEA) and the average residual wavelet coefficient (ARWC) are developed from AE signals as novel indicators to reflect the tribological behaviours.\ud Both the hard threshold based LEA and wavelet packet transform LEA values allow two different new lubricants to be diagnosed in accordance with model predictions whereas they produce less consistent results in differentiating the used oil under several operating conditions. Nevertheless, ARWC can separate the used oil successfully in that it can highlight the AAC effects of particle collisions in used oils. Similarly, LEA shows little impacts of two alternative fuels on the tribological behaviours. However, ARWC shows significantly higher amplitudes in several operating conditions when more particles can be produced due to unstable and incomplete combustions of both the biodiesel and FT diesel, compared with pure diesel, indicating they can cause light wear

An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

Energy analysis of a hybrid electro-hydraulic system for efficient mobile hydraulics

Energy efficiency plays a significant role in mobile hydraulics due to the high amount of carbon dioxide and pollutants being released into the atmosphere. Efficiency improvements are urgently needed, so the electrification of mobile hydraulics represents a fantastic opportunity in this regard. This approach leads to electro-hydraulic systems that remove functional flow throttling in control valves and enable energy recovery. Fuels savings were already demonstrated in simulation, but the literature does not offer entire energy analyses of these electro-hydraulic solutions. This limitation prevents complete system-level comprehension and does not give enough insight to pinpoint areas for further efficiency improvements. Thus, this paper focuses on a hybrid system for excavators based on electro-hydraulic drives that is compared against the original valve-controlled layout. The objective is to quantify the energy flows insight the excavator during relevant operations and highlight the resulting energy losses. The outcomes confirm that electro-hydraulic solutions are suitable for a low-carbon economy. They indicate hydraulic actuators, speed-controlled pumps, and electric motors as the critical components for further energy efficiency enhancement excluding the combustion engine

A Comparative Analysis of Biodiesel and Diesel Emissions

The goals of this project were to identify differences in the composition of combustion emissions between diesel and biodiesel and to determine if an emissions meter would be a suitable addition to a laboratory experiment. This was achieved by testing combustion emissions of the two fuels and mixtures using a flue gas analyzer. Clear trends were identified between biodiesel proportions and exhaust concentrations of carbon monoxide, carbon dioxide, and nitrogen oxides, as well as the effect of temperature

Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform

This work focuses on a method which experimentally recognizes faults of gearboxes using wavelet packet and two support vector machine models. Two wavelet selection criteria are used. Some statistical features of wavelet packet coefficients of vibration signals are selected. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Eventually, the gearbox faults are classified using these statistical features as input to least square support vector machine (LSSVM) and wavelet support vector machine (WSVM). Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the WSVM identified the fault categories of gearbox more accurately and has a better diagnosis performance as compared to the LSSVM