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

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications

Progress towards a Framework for Aerospace Vehicle Reasoning (FAVER)

This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form of symptom vectors, to detect and isolate cascading faults and their root causes. The blending of a digital twin and reasoning in this framework will enable FAVER to: i) isolate faults that have both local and cascading effects on the concerned systems, ii) identify faults that were previously unknown, and iii) resolve ambiguous faults. This paper explains the different steps involved in developing FAVER and how this framework can be demonstrated in the aforementioned scenarios with the help of different use cases. This paper also talks about the challenges to be faced while developing this framework and ways to overcome them

Intelligent comprehensive control and monitor of proton exchange membrane fuel cell for hybrid UPS system

This paper, to improve the performance of a Proton Exchange Membrane fuel cell (PEMFC) stack, avoid the hydrogen and oxygen/air starvation of electrochemical reaction and the performance deterioration of the stack, prevent the dehydration and drying of the membrane, keep the water content in the membrane, heighten the utilization of the gases, and track the output power of a hybrid uninterruptible power supply (UPS) system with backup PEMFC and battery power sources, conducts research in the dynamic model, the on-line parameters monitoring of PEMFC, such as the resistance in the PEMFC stack using the current interrupt method and the performance improvement of the PEMFC employing an intelligent comprehensive control strategy of the operation parameters, such as operating temperature, pressures and mass flows of hydrogen and air, the output current and voltage for the PEMFC stack, the power supply switching between PEMFC and battery. The intelligent comprehensive control and monitor method is proposed and applied to the PEMFC generating system employed for the power source of UPS. The experimental results show that the proposal method can effectively monitor and control the pressures of the inlet hydrogen and the operating temperature of the stack, automatically switch the power supply between PEMFC and battery, efficaciously prevent the destroy of the stack when the load changes sharply, the hydrogen is purged and the output current is interrupted regularly, and reasonably improve the performance of the PEMFC through the water balance and thermal management, and real-time realize the tracking for the changes of the output power and the distribution of the mass flow rates of hydrogen and air. © 2009 IEEE

Performance analysis and dynamics of innovative SOFC hybrid systems based on turbocharger-derived machinery

La crescente consapevolezza su temi quali il cambiamento climatico e l\u2019inquinamento atmosferico ha portato a politiche nazionali ed internazionali mirate allo sviluppo di sistemi energetici innovativi e sostenibili. Tra di essi, le fuel cell sono uno dei pi\uf9 promettenti, essendo caratterizzate da alte efficienze e basse emissioni. In particolare, i sistemi ibridi basati sull\u2019integrazione di fuel cell ad alta temperatura con dispositivi derivati da turbocompressori hanno attirato l\u2019attenzione del mondo accademico e dell\u2019industria negli ultimi decenni. Tuttavia, la complessit\ue0, la fragilit\ue0 e l\u2019alto costo di questi impianti ha rallentato il loro sviluppo, e solo poche grandi aziende sono state in grado di realizzare prototipi completi. Le difficolt\ue0 tecniche affrontate dalla comunit\ue0 scientifica hanno messo in luce l\u2019importanza delle simulazioni per progettare, testare, controllare e analizzare i sistemi ibridi a fuel cell. Sulla base di tale esperienza, questa tesi mira ad espandere la attuale conoscenza sui sistemi ibridi a fuel cell a ossidi solidi, ponendo una particolare attenzione su un innovativo sistema turbocompresso di piccola taglia, alimentato con biogas e recentemente introdotto all\u2019interno del progetto europeo Bio-HyPP. Lo scopo principale della tesi \ue8 determinare se questo tipo di sistema possa essere una valida alternativa ai sistemi basati su microturbine a gas, analizzando il suo comportamento in relazione a diversi scenari, sia stazionari, sia transitori. Per fare ci\uf2, \ue8 necessario definire i vincoli operativi del sistema e sviluppare un sistema di controllo in grado di rispettarli, ottimizzando al tempo stesso le prestazioni dell\u2019impianto. Inoltre, l\u2019affidabilit\ue0 dei sistemi ibridi pu\uf2 essere migliorata grazie all\u2019implementazione di strumenti diagnostici e di procedure per prevenire il pompaggio del compressore. La parte finale della tesi \ue8 mirata allo studio di tali strumenti, al loro sviluppo e alla loro integrazione con il sistema di controllo. Tutte le attivit\ue0 presentate in questa tesi sono state svolte facendo affidamento su strumenti di simulazione. Ci\uf2 \ue8 stato possibile grazie alla collaborazione tra il Laboratorio di Matematica Applicata, Simulazione e Modellistica Matematica e il Thermochemical Power Group dell\u2019Universit\ue0 degli Studi di Genova. Dopo aver presentato il layout del sistema a fuel cell con turbocompressore, un dettagliato modello stazionario dell\u2019impianto sviluppato in Matlab\uae-Simulink\uae \ue8 stato utilizzato per progettare una strategia, basata sul controllo di valvole installate sull\u2019impianto, in grado di rispettare tutti i suoi vincoli operativi. Successivamente, \ue8 stata svolta un\u2019analisi di prestazioni in off-design, considerando allo stesso tempo diverse condizioni di carico di potenza e di temperatura ambiente. Tale analisi \ue8 stata utilizzata per confermare l\u2019efficacia della strategia di controllo proposta, e per valutare le capacit\ue0 del sistema con turbocompressore. Successivamente \ue8 stato creato un modello dinamico utilizzando lo strumento TRANSEO, in modo da studiare il comportamento del sistema durante i transitori. Avendo adottato una strategia di controllo basata sulla valvola di cold bypass, \ue8 stata analizzata la risposta del sistema ad una sua apertura a gradino, al fine di progettare un sistema di controllo efficace e reattivo, in grado di mantenere la massima temperatura di cella costante e, allo stesso tempo, di rispettare i vincoli del sistema. Sono stati progettati quattro diversi controllori, che successivamente sono stati testati su due diversi scenari di variazione di carico e confrontati sulla base di vari parametri operativi. La parte finale della tesi ha riguardato lo sviluppo di innovativi strumenti che possano aumentare l\u2019affidabilit\ue0 dei sistemi ibridi a fuel cell a ossidi solidi, in particolare tecniche di prevenzione del pompaggio e sistemi di diagnostica basati su reti Bayesiane. Un modello semplificato del sistema con turbocompressore \ue8 stato sviluppato in TRANSEO e sono state testate diverse tecniche di prevenzione del pompaggio: condizionamento del flusso d\u2019aria, iniezione di acqua, ricircolo e bleed, installazione di un eiettore all\u2019imbocco del compressore. Le soluzioni pi\uf9 efficaci sono state integrate con il controllore del sistema ibrido e sono state testate durante un transitorio per evitare che il punto operativo del compressore si avvicinasse al pompaggio. Infine, grazie ad una collaborazione tra l\u2019Universit\ue0 degli Studi di Genova e la M\ue4lardalens H\uf6gskola di V\ue4ster\ue5s, in Svezia, sono state sviluppate delle reti Bayesiane per la diagnostica di sistemi ibridi a fuel cell a ossidi solidi con microturbina a gas. Questa attivit\ue0 \ue8 stata svolta simulando il sistema su Matlab\uae-Simulink\uae e creando le reti Bayesiane su Hugin Expert. Due sistemi di diagnostica, uno per la microturbina e uno per la fuel cell, sono stati sviluppati e testati in condizioni stazionarie. Il secondo \ue8 stato anche testato in condizioni dinamiche e integrato con il sistema di controllo per prevenire l\u2019usura della cella. In conclusione, questa tesi ha messo in luce il grande potenziale dei sistemi ibridi SOFC-turbocompressore, mostrando la loro alta efficienza in un ampio intervallo di condizioni operative in termini di carico elettrico e temperatura ambiente. La tesi ha anche dimostrato che \ue8 possibile garantire il corretto funzionamento di questi sistemi durante diversi scenari transitori, implementando controllori a cascata progettati per agire sulla valvola di bypass freddo per controllare la massima temperatura della cella. Per quanto riguarda la possibilit\ue0 di migliorare l\u2019affidabilit\ue0 di tali sistemi, le tecniche basate sul ricircolo del compressore sono risultate essere le pi\uf9 efficaci per allontanare il sistema da una condizione di pompaggio. I risultati delle simulazioni mostrano come la loro integrazione con strumenti di monitoraggio possa prevenire diverse situazioni di pericolo. La parte finale della tesi ha mostrato come il deterioramento dei sistemi ibridi a SOFC possa essere limitato grazie a reti Bayesiane, che sono state utilizzate per diagnosticare accuratamente le condizioni di un sistema SOFC-microturbina a gas, ma potrebbero ugualmente essere applicate su impianti con turbocompressore.The growing awareness on climate change and pollution has brought to national and international policies aimed at promoting the development of innovative and environmentally sustainable energy systems. Among these systems, fuel cells are one of the most promising technologies, characterized by high energy conversion efficiencies and low emissions. In particular, hybrid systems based on the integration of a high temperature fuel cell with turbocharger-derived machinery have drawn the interest of academia and industry over the past decades. However, the complexity, fragility and high cost of these plants have slowed down their development, and only a few big companies were able to build complete prototypes. The technological challenges faced by the scientific community have highlighted the importance of simulations to design, test, control and analyse fuel cell hybrid systems. Based on this experience, this thesis wants to expand the current knowledge on solid oxide fuel cell hybrid systems, with a particular focus on an innovative small-scale biofueled turbocharged layout, which was introduced recently within the Bio-HyPP European project. The main goal of this thesis is to determine if this kind of system can be a viable alternative to micro gas turbine-based systems, analysing its steady-state and transient behaviour in various operating conditions. To do this, it is necessary to define the system operative constraints, and to develop a control system capable of ensuring their compliance, while optimizing the plant performance. The possibility of increasing the reliability of solid oxide fuel cell hybrid systems is finally investigated, considering the implementation of surge prevention techniques and diagnostic tools. All these activities strongly relying on simulation tools. This was possible thanks to the collaboration between the Laboratory of Applied Mathematics, Simulation and Mathematical Modelling with the Thermochemical Power Group of the University of Genoa. After introducing the layout of the turbocharged fuel cell system, a detailed steady-state model of the plant is developed in Matlab\uae-Simulink\uae and used to design a strategy, based on the control of valves installed on the plant, able to comply with its many operative constraints. Then, an off-design performance analysis of the system is performed, considering simultaneously various conditions of power load and ambient temperature. This analysis is used to confirm the effectiveness of the proposed control strategy and to assess the capabilities of the turbocharged system. A dynamic model is created using the TRANSEO tool to study the transient behaviour of the system. Having adopted a control strategy based on the cold bypass valve, the response of the system to a valve opening step change is analysed in order to design an effective and responsive control system, able to keep the fuel cell maximum temperature constant while complying with the system constraints. Four different controllers are designed, tested on two different load variation scenarios and compared on the basis of many parameters. The final part of the thesis regards the development of innovative tools aimed at improving the reliability of solid oxide fuel cell hybrid system, in particular surge prevention techniques and Bayesian belief network-based diagnosis systems. A simplified dynamic model of the turbocharged SOFC system is developed in TRANSEO, and various surge prevention techniques are tested on it: intake air conditioning, water spray at compressor inlet, air bleed and recirculation, and installation of an ejector at the compressor intake. The most effective procedures are integrated with the controller of the hybrid system and tested during a transient scenario to prevent the compressor operative point from approaching a surge condition. Bayesian belief networks aimed at diagnosing the status of SOFC hybrid systems are developed thanks to a collaboration between the University of Genoa and the M\ue4lardalens H\uf6gskola of V\ue4ster\ue5s, Sweden. A micro gas turbine \u2013 solid oxide fuel cell system is considered for this study, but the methodology could be easily extended to turbocharged plants. The activity is carried out simulating the system on Matlab\uae-Simulink\uae and designing the Bayesian networks on Hugin Expert. Two different diagnosis systems, one for the turbomachinery and one for the fuel cell stack, are developed and tested on stationary conditions. The second one is also tested during transients and integrated with the control system to prevent degradation of the fuel cells. In conclusion, this thesis highlighted the great potential of turbocharged SOFC hybrid systems, showing high energy conversion efficiencies in a wide operative range in terms of load and ambient conditions. It also showed that the proper operation of the system is possible during various transient scenarios, implementing cascade controllers designed to act on a cold bypass valve to control the SOFC maximum temperature. Regarding the possibility of improving the reliability of these systems, surge prevention techniques based on compressor recirculation appeared as the most effective ones. Simulation results suggest that their integration with a surge precursors detection tool could avoid the occurrence of many potentially dangerous scenarios. The final part of this thesis showed that the durability of SOFC hybrid systems could be further improved thanks to Bayesian belief networks, which were proved to effectively diagnose the status of SOFC-MGT systems but could be applied to turbocharged plants as well

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

An ‘on-demand’ Data Communication Architecture for Supplying Multiple Applications from a Single Data Source: An Industrial Application Case Study

A key aspect of automation is the manipulation of feedback sensor data for the automated control of particular process actuators. Often in practice this data can be reused for other applications, such as the live update of a graphical user interface, a fault detection application or a business intelligence process performance engine in real-time. In order for this data to be reused effectively, appropriate data communication architecture must be utilised to provide such functionality. This architecture must accommodate the dependencies of the system and sustain the required data transmission speed to ensure stability and data integrity. Such an architecture is presented in this paper, which shows how the data needs of multiple applications are satisfied from a single source of data. It shows how the flexibility of this architecture enables the integration of additional data sources as the data dependencies grow. This research is based on the development of a fully integrated automation system for the test of fuel controls used on civil transport aircraft engines

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

Polymer electrolyte fuel cells (PEFCs) are regarded as a substitution for the combustion engine with high energy conversion efficiency and zero CO2 emissions. Stable system operation requires control within a relatively narrow range of operating conditions to achieve the optimal output, leading to faults that can easily cause accelerated degradation when operating conditions deviate from the control targets. Performance recovery of the system can be realized through early fault diagnosis; therefore, accurate and effective diagnostic characterisation is vital for long-term serving. A review of off-line and on-line techniques applied to the fault diagnosis of fuel cells is presented in this work. Off-line approaches include electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), galvanostatic charge (GSC), visualisation-based and image-based techniques; the on-line methods can be divided into model-based, data-driven, signal-based and hybrid methods. Since each methodology has advantages and drawbacks, its effectiveness is analysed, and limitations are highlighted

Monitoring of the piston ring-pack and cylinder liner interface in diesel engines through acoustic emission measurements

Investigation of novel condition monitoring systems for diesel engines has received much recent attention due to the increasing demands placed upon engine components and the limitations of conventional techniques. This thesis documents experimental research conducted to assess the monitoring capabilities of Acoustic Emission (AE) analysis. In particular it focuses on the possibility of monitoring the piston ring-pack and cylinder liner interface, a critical engine sub-system for which there are currently few practical monitoring options. A series of experiments were performed on large, two-stroke and small, four-stroke diesel engines. Tests under normal operating conditions developed a detailed understanding of typical AE generation in terms of both the source mechanisms and the characteristics of the resulting activity. This was supplemented by specific tests to investigate possible AE generation at the ring-pack/liner interface. For instance, for the small engines measures were taken to remove known AE sources in order to accentuate any activity originating at the interface whilst for the large engines the interfacial conditions were purposely deteriorated through the removal of the lubricating oil supply to one cylinder. Interpretation of the results was based mainly upon comparisons with published work encompassing both the expected ring-pack behaviour and AE generation from tribological processes. This provided a strong indication that the source of the ring-pack/liner AE activity was the boundary frictional losses. The ability to monitor this process may be of significant benefit to engine operators as it enhances the diagnostic information currently available and may be incorporated into predictive maintenance strategies. A further diagnostic technique considered was the possibility of using AE parameters combined with information of crankshaft speed fluctuations to evaluate engine balance and identify underperforming cylinders.EU Competitive and Sustainable Growth Programme, Project no: GRD2-2001-5001