A review of model based and data driven methods targeting hardware systems diagnostics

System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

A robust fault diagnosis and forecasting approach based on Kalman filter and interval type-2 fuzzy logic for efficiency improvement of centrifugal gas compressor system

The paper proposes a robust faults detection and forecasting approach for a centrifugal gas compressor system, the mechanism of this approach used the Kalman filter to estimate and filtering the unmeasured states of the studied system based on signals data of the inputs and the outputs that have been collected experimentally on site. The intelligent faults detection expert system is designed based on the interval type-2 fuzzy logic. The present work is achieved by an important task which is the prediction of the remaining time of the system under study to reach the danger and/or the failure stage based on the Auto-regressive Integrated Moving Average (ARIMA) model, where the objective within the industrial application is to set the maintenance schedules in precisely time. The obtained results prove the performance of the proposed faults diagnosis and detection approach which can be used in several heavy industrial systemsPeer ReviewedPostprint (published version

Modelling and optimisation of decentralised hybrid solar biogas system to power an organic Rankine cycle (ORC-Toluene) and air gap membrane distillation (AGMD) for desalination and electric power generation

The intensive use of fossil fuels to meet the world energy and water demand has caused several environmental issues, such as global warming, air pollution and ozone depletion. Therefore, the integration of stand-alone decentralised hybrid renewable energy systems is a promising solution to satisfy the global energy-water demands and minimize the effects of fossil fuels utilisation. Among these hybrid technologies, concentrated solar power (CSP) combined with waste-based biogas to power organic Rankine cycle for cogeneration provide the means to generate dispatchable, reliable, renewable electricity and water in high direct normal incidence (DNI) regions around the world. Due to the strong inverse correlation between DNI resources and freshwater availability, most of the best potential CSP regions also lack sufficient freshwater resources. The current study proposes and applies a novel multi-dimensional modelling technique based on artificial neural networks (ANN) for hourly solar radiation and wind speed data forecasting over six locations in Oman. The developed model is the first attempt to integrate two ANN models simultaneously by using enormous meteorological data points for both solar radiation and wind speed prediction. The developed model requires only three parameters as inputs, and it can predict solar radiation and wind speed data simultaneously with high accuracy. As a result, the model provides a user-friendly interface that can be utilised in the energy systems design process. Consequently, this model facilitates the implementation of renewable energy technologies in remote areas in which gathering of weather data is challenging. Meanwhile, the accuracy of the model has been tested by calculating the mean absolute percentage error (MAPE) and the correlation coefficient (R). Therefore, the model developed in this study can provide accurate weather data and inform decision makers for future instalments of energy systems. Furthermore, a novel proposed hybrid solar and biogas system for desalination and electric power generation using advanced modelling techniques to integrate the stand-alone off-grid system has been designed. The novelty emerges from some facts, which are centralised around the use of a hybrid electric generation via Concentrated Solar Power (CSP) and anaerobic digestion biogas to achieve higher stability and profitability. Meanwhile, the cogeneration through the waste heat of the ORC drives the AGMD, which benefits as well from the higher stability due to hybridisation. In addition, an innovative and user-friendly modelling approach has been applied, and this efficiently integrates the individual energy components, i.e. PTC, anaerobic biogas boiler, ORC and AGMD, which fosters the optimisation of the proposed system. The models have been developed in the MATLAB/Simulink® software and have been used to investigate the system area, dimensions, and cost and to ensure that the electrical and water demand of the end-user are met. In addition, a new detailed thermo-economic assessment of the proposed hybrid solar biogas for cogeneration in off-grid applications has been investigated. An energy, exergy, and cost analysis has been performed and to fully utilise this, a sensitivity assessment on the developed model has been analysed to examine the effects of various design parameters on the thermo-economic performance. Finally, implementing an in-depth simulation testing of the system in a rural region in Oman is presented. The novel integrated solar and biogas system that has been designed through advanced modelling in the MATLAB/ Simulink® is integrated with a robust multi-objective optimisation technique to determine the best operating configuration. Three objective functions namely, maximising power and water production, and minimising the unit exergy product costs have been formulated. The turbine efficiency, top ORC vapor temperature and ORC condenser temperature has been selected as the decision variables. The non-dominated sorting genetic algorithm (NSGA-II) has been employed to solve the optimisation problem and produce a Pareto frontier of the optimal solutions. Further, the TOPSIS approach has been used to select the optimal solution from the Pareto set. The study constitutes the first attempt to holistically optimise such a hybrid off-grid cogeneration system in a robust manner