Research on Improvement and Applications for Bayesian Fault Diagnosis

控制回路故障检测与诊断有助于保证生产过程的安全和高效、降低维护费用和减少停机时间。贝叶斯诊断是控制回路监测的概率化诊断框架，它能够综合多个监测器技术，以构建诊断系统进而作出最优决策。然而，工业过程控制回路诊断中存在许多不同的实际情况，严重制约了贝叶斯诊断的性能。本文重点从数据降维、似然估计等方面研究改进贝叶斯诊断性能的方法，提出了基于优化直方图估计的证据离散化方法、基于线性判别分析的特征提取与降维以及平均移动似然估计方法。通过仿真系统、工业基准数据和工业规模系统的仿真实验，验证了所提方法的有效性。论文主要包含以下几个方面的工作： (1) 综述了现有的贝叶斯诊断方法及其研究现状，系统介绍了控制...The purpose of control loop detection and diagnosis is to ensure the safety and efficacy of the production process, reduce maintenance costs and downtime. Bayesian diagnosis is a probabilistic diagnosis framework of control loop monitoring, which can combine multiple monitor technology to build a diagnosis system and make an optimal decision. However, there are many different situations in the con...学位：工程硕士院系专业：航空航天学院_工程硕士(控制工程)学号：2322013115337

Fault detection using transfer function techniques

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Real-time implementation of a sensor validation scheme for a heavy-duty diesel engine

With ultra-low exhaust emissions standards, heavy-duty diesel engines (HDDEs) are dependent upon a myriad of sensors to optimize power output and exhaust emissions. Apart from acquiring and processing sensor signals, engine control modules should also have capabilities to report and compensate for sensors that have failed. The global objective of this research was to develop strategies to enable HDDEs to maintain nominal in-use performance during periods of sensor failures. Specifically, the work explored the creation of a sensor validation scheme to detect, isolate, and accommodate sensor failures in HDDEs. The scheme not only offers onboard diagnostic (OBD) capabilities, but also control of engine performance in the event of sensor failures. The scheme, known as Sensor Failure Detection Isolation and Accommodation (SFDIA), depends on mathematical models for its functionality. Neural approximators served as the modeling tool featuring online adaptive capabilities. The significance of the SFDIA is that it can enhance an engine management system (EMS) capability to control performance under any operating conditions when sensors fail. The SFDIA scheme updates models during the lifetime of an engine under real world, in-use conditions. The central hypothesis for the work was that the SFDIA scheme would allow continuous normal operation of HDDEs under conditions of sensor failures. The SFDIA was tested using the boost pressure, coolant temperature, and fuel pressure sensors to evaluate its performance. The test engine was a 2004 MackRTM MP7-355E (11 L, 355 hp). Experimental work was conducted at the Engine and Emissions Research Laboratory (EERL) at West Virginia University (WVU). Failure modes modeled were abrupt, long-term drift and intermittent failures. During the accommodation phase, the SFDIA restored engine power up to 0.64% to nominal. In addition, oxides of nitrogen (NOx) emissions were maintained at up to 1.41% to nominal

Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

A performance evaluation framework for building fault detection and diagnosis algorithms

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products—a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms

A review of electrostatic monitoring technology: The state of the art and future research directions

Electrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for system health management. It encompasses three key research areas: sensor technology; signal detection, processing and feature extraction; and verification experimentation. It has received considerable recent attention for condition monitoring due to its ability to provide warning information and non-obstructive measurements on-line. A number of papers in recent years have covered specific aspects of the technology, including sensor design optimization, sensor characteristic analysis, signal de-noising and practical applications of the technology. This paper provides a review of the recent research and of the development of electrostatic monitoring technology, with a primary emphasis on its application for the aero-engine gas path. The paper also presents a summary of some of the current applications of electrostatic monitoring technology in other industries, before concluding with a brief discussion of the current research situation and possible future challenges and research gaps in this field. The aim of this paper is to promote further research into this promising technology by increasing awareness of both the potential benefits of the technology and the current research gaps

Diesel Engine

Diesel engines, also known as CI engines, possess a wide field of applications as energy converters because of their higher efficiency. However, diesel engines are a major source of NOX and particulate matter (PM) emissions. Because of its importance, five chapters in this book have been devoted to the formulation and control of these pollutants. The world is currently experiencing an oil crisis. Gaseous fuels like natural gas, pure hydrogen gas, biomass-based and coke-based syngas can be considered as alternative fuels for diesel engines. Their combustion and exhaust emissions characteristics are described in this book. Reliable early detection of malfunction and failure of any parts in diesel engines can save the engine from failing completely and save high repair cost. Tools are discussed in this book to detect common failure modes of diesel engine that can detect early signs of failure

Análisis de metodologías de modelado de motores diésel marinos para su diagnosis y mantenimiento basado en condiciones

313 p.La presente tesis doctoral analiza las diferentes estrategias de modelado de motores diésel marinos existentes en el marco de su diagnosis y mantenimiento basado en condiciones. Concretamente, tras una fase preliminar en la que se analizan y prueban experimentalmente el modelo termodinámico y el modelo de redes neuronales artificiales en un motor diésel de un cilindro, se concluye que ambos muestran niveles de error relativamente bajos pero que el modelo termodinámico requiere tiempos de cálculo notablemente mayores. Partiendo de esta base, consecutivamente se ha modelado un motor diésel marino de 6 cilindros empleando las redes neuronales artificiales. En este motor, se ha analizado el nivel de precisión de las redes neuronales para cualquier condición del motor dentro de su mapa de funcionamiento completo (considerando el efecto de factores externos como la meteorología o corrientes) para calcular 35 parámetros de funcionamiento y emisiones simultáneamente. Entre los resultados, las redes neuronales han mostrado errores por debajo del 8,5% para todos los parámetroscalculados a excepción de las emisiones de CO y NO2 que debido a sus bajas concentraciones en este tipo de motores han mostrado errores mayores. Se ha demostrado pues, el potencial de este tipo de metodología para mostrar la imagen completa del estado de un motor marino para cualquier punto dentro de su mapa de funcionamiento y con tiempos de cálculo admisibles para procesos de modelado en tiempo real (0,109 segundos). Por último, se ha analizado la actividad de un motor diésel de 9 cilindros a bordo de un buque atunero operativo durante los años 2020 y 2021, procesando hasta 5.181 horas de funcionamiento sin ningún fallo. Por consiguiente, se han calculado los valores límite de las desviaciones que puedan sufrir 57 parámetros diferentes del motor dentro de su funcionamiento correcto, con objeto de proveer una referencia útil para establecer límites de desviaciones representativas de fallos en futuros trabajos de investigación en el campo de la diagnosis y el mantenimiento basado en condiciones de los motores diésel marinos.AZT