Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise. This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the time-frequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings

Modeling and Control for Smart Grid Integration of Solar/Wind Energy Conversion System

Performance optimization, system reliability and operational efficiency are key characteristics of smart grid systems. In this paper a novel model of smart grid-connected PV/WT hybrid system is developed. It comprises photovoltaic array, wind turbine, asynchronous (induction) generator, controller and converters. The model is implemented using MATLAB/SIMULINK software package. Perturb and observe (P&O) algorithm is used for maximizing the generated power based on maximum power point tracker (MPPT) implementation. The dynamic behavior of the proposed model is examined under different operating conditions. Solar irradiance, temperature and wind speed data is gathered from a grid connected, 28.8kW solar power system located in central Manchester. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for smart grid performance optimization

A new approach to the cohesive zone model that includes thermal effects

© 2019 This study presents a cohesive zone model combining mechanical and thermal effects. Thermal stress was added to the Helmholtz free energy density in order to derive a new approach to incremental damage which included the effect of temperature. The developed damage model has been implemented in ABAQUS using the UMAT subroutine and applied of two different specimens; a three-point bending specimen and a Double Cantilever Beam. The effectiveness of the new method was tested for the given specimens at different temperatures. The simulation results revealed that the total energy of the interface element of high strength carbon fiber reinforced plastic increased as its temperature decreased. It is demonstrated that the load-displacement curves obtained from the numerical model for both test specimens were in good agreement with experimental data available in literature

Adoption of MEMS technology in e-maintenance systems for rotating machinery

Microelectromechanical systems (MEMS)-based sensing networks with on-board signal processing capabilities are becoming very attractive for monitoring the condition of rotating and static equipment. Their advantages in cost and size are an important factor for deployment in a new generation of maintenance called e-maintenance. In this paper, an intelligent monitoring system for e-maintenance (IMSEM) was developed using MEMS accelerometers, a low-power microprocessor and a wireless communication module. The system has a compatible framework and interface with open system architecture for condition-based maintenance (OSA-CBM). It integrates OSA-CBM-defined functions, including a sensing module, signal processing, condition monitoring and health assessment. Thus, the developed system successfully reduced the monitoring complexity and communication overhead with human operators. The performance of IMSEM is evaluated by carrying out fault diagnostics on the rotating unbalance of a mechanical shaft driven by a direct current (DC) motor with varying load and speed. Five statistical features were calculated for 63 vibration datasets. 25 datasets were used to train a support vector machine, with a linear kernel, and the other 38 sets were used for binary classification. About 94.7% of unknown conditions were successfully classified as healthy/unhealthy based on all features, which was improved to 100% using the best three features. This has demonstrated the capability of the developed system in detecting faults and the severity of rotor unbalance, based on ISO 1940-1:2003

Effective Technique for Improving Electrical Performance and Reliability of Fuel Cells

To optimise the electrical performance of proton exchange membrane (PEM) fuel cells, a number of factors have to be precisely monitored and controlled. Water content is one of those factors that has great impact on reliability, durability and performance of PEM fuel cells. The difficulty in controlling water content lies in the inability to determine correct level of water accumulated inside the fuel cell. In this paper, a model-based technique, implemented in COMSOL, is presented for monitoring water content in PEM fuel cells. The model predicts, in real time, water content taking account of other processes occurring in gas channels, across gas diffusion layers (GDL), electrodes, and catalyst layer (CL) and within the membrane to minimize voltage losses and performance degradation. The level of water generated is calculated as function of cell’s voltage and current. Model’s performance and accuracy are verified using a transparent 500 mW PEM fuel cell. Results show model predicted current and voltage curves are in good agreement with the experimental measurements. The unique feature of this model is that, no special requirements are needed as only current, and voltage of the PEM fuel cell were measured thus, is expected to pave the path for developing non-intrusive control and monitoring systems for fuel cells

Damage degradation modelling for transverse cracking in composite laminates under low-velocity impact

This paper derives a damage evaluation law for fibres and an expression for the damage parameter function. It also proposes an approach to the matrix damage degradation law. A proposed approach to both the constitutive damage degradation model and increment law is developed to predict intralaminar damage evolution in composite laminates. Failure envelopes for different failure criteria are discussed in term of the fracture plane of matrix cracking under compressive load. The damage surface consistency condition is applied to derive a plastic multiplier as a function of the damage plastic flow so that the plastic strain is updated at each time increment and the stress–strain constitutive relationship of the damage model will also be updated. A user-defined subroutine has been adopted to implement a proposed constitutive damage degradation model. The effectiveness of the proposed method has been examined under low velocity impact. The numerical findings confirm that results obtained using the suggested approach are in good agreement with experimental results

Progressive failure mechanism of laminated composites under fatigue loading

© The Author(s) 2020. A cohesive zone model for delamination propagation in laminated composites under static and fatigue loading has been derived and validated with experimental data under different mode conditions. This study presents a new approach to quantify fatigue delamination degradation based on damage mechanics to evaluate the rate of fatigue damage ((Formula presented.)). The static damage evaluation and fatigue damage degradation are derived from damage surface concept. Both static and fatigue damage linked each other to establish fatigue crack growth formula in the laminated composites. A user-defined subroutine, UMAT, has been employed to develop and implement a damage model in ABAQUS. Two different specimens; a double cantilever beam and a single lap joint were used to investigate the effectiveness of the new method. The simulation results revealed that the developed model had good agreement with experimental data available in literature

Effect of friction and shear strength enhancement on delamination prediction

This study considers the intra-laminar damage mode in composite structures and its effect on delamination prediction. The progressive damage models for matrix cracking and fibre failure in ABAQUS, based on Hashin’s model, are only available for shell elements. The results presented here show that the predicted matrix cracking based on the damage model presently available in ABAQUS diverges from experimental results. A new model based on strain failure criteria, which can be used with both shell elements and 3D solid elements, has been developed. The effect of friction coefficient and enhancement factor on the delamination lobes within the delamination area was investigated, and it is shown that the intact zone can be captured in laminate [03/903]s and [903/03]s subjected to low-velocity impact, by using an enhancement factor of ¼ 0.75, and friction coefficient 0:5, together with the new model proposed here

Development of Novel Big Data Analytics Framework for Smart Clothing

© 2013 IEEE. Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested

Coordinate Transformation-Free Observer-Based Adaptive Estimation of Distorted Single-Phase Grid Voltage Signal

© 2013 IEEE. This paper studies the phase and frequency estimation problem of single-phase grid voltage signal in the presence of DC offset and harmonics. For this purpose, a novel parameterized linear model of the grid voltage signal is considered where the unknown frequency of the grid is considered as the parameter. Based on the developed model, a linear observer (Luenberger type) is proposed. Then using Lyapunov stability theory, an estimator of the unknown grid frequency is developed. In order to deal with the grid harmonics, multiple parallel observers are then proposed. The proposed technique is inspired by other Luenberger observers already proposed in the literature. Those techniques use coordinate transformation that requires real-time matrix inverse calculation. The proposed technique avoids real-time matrix inversion by using a novel state-space model of the grid voltage signal. In comparison to similar other techniques available in the literature, no coordinate transformation is required. This significantly reduces the computational complexity w.r.t. similar other techniques. Comparative experimental results are provided with respect to two other recently proposed nonlinear techniques to show the dynamic performance improvement. Experimental results demonstrate the suitability of the proposed technique