Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

ELECTRONIC PROGNOSTICS AND HEALTH MANAGEMENT: A RETURN ON INVESTMENT ANALYSIS

Prognostics and Health Management (PHM) provides the potential to lower sustainment costs, to improve maintenance decision-making, and to provide product usage feedback into the product design and validation process. A case analysis was developed using a discrete event simulation to determine the benefits and the potential cost avoidance resulting from the use of PHM in avionics. The model allows for variability in implementation costs, operational profile, false alarms, random failure rates, and system composition to enable a comprehensive calculation of the Return on Investment (ROI) in support of acquisition decision making. The case analysis compared the life cycle costs using unscheduled maintenance to the life cycle costs using two types of PHM approaches

A "DESIGN FOR AVAILABILITY" METHODOLOGY FOR SYSTEMS DESIGN AND SUPPORT

Prognostics and Health Management (PHM) methods are incorporated into systems for the purpose of avoiding unanticipated failures that can impact system safety, result in additional life cycle cost, and/or adversely affect the availability of a system. Availability is the probability that a system will be able to function when called upon to do so. Availability depends on the system's reliability (how often it fails) and its maintainability (how efficiently and frequently it is pro-actively maintained, and how quickly it can be repaired and restored to operation when it does fail). Availability is directly impacted by the success of PHM. Increasingly, customers of critical systems are entering into "availability contracts" in which the customer either buys the availability of the system (rather than actually purchasing the system itself) or the amount that the system developer/manufacturer is paid is a function of the availability achieved by the customer. Predicting availability based on known or predicted system reliability, operational parameters, logistics, etc., is relatively straightforward and can be accomplished using several methods and many existing tools. Unfortunately in these approaches availability is an output of the analysis. The prediction of system's parameters (i.e., reliability, operational parameters, and/or logistics management) to meet an availability requirement is difficult and cannot be generally done using today's existing methods. While determining the availability that results from a set of events is straightforward, determining the events that result in a desired availability is not. This dissertation presents a "design for availability" methodology that starts with an availability requirement and uses it to predict the required design, logistics and operations parameters. The method is general and can be applied when the inputs to the problem are uncertain (even the availability requirement can be represented as a probability distribution). The method has been demonstrated on several examples with and without PHM

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

In-situ health monitoring of IGBT power modules in EV applications

Power electronics are an enabling technology and play a critical role in the establishment of an environmentally-friendly and sustainable low carbon economy. The electrification of passenger vehicles is one way of achieving this goal. It is well acknowledged that Electric vehicles (EVs) have inherent advantages over the conventional internal combustion engine (ICE) vehicles owing to the absence of emissions, high efficiency, and quiet and smooth operation. Over the last 20 years, EVs have improved significantly in their system integration, dynamic performance and cost. It has attracted much attention in research communities as well as in the market. In 2011 electric vehicle sales were estimated to reach about 20,000 units worldwide, increasing to more than 500,000 units by 2015 and 1.3 million by 2020 which accounts for 1.8 per cent of the total number of passenger vehicles expected to be sold that year. In general, electric vehicles use electric motors for traction drive, power converters for energy transfer and control, and batteries, fuel cells, ultracapacitors, or flywheels for energy storage. These are the core elements of the electric power drive train and thus are desired to provide high reliability over the lifetime of the vehicle. One of the vulnerable components in an electric power drive train is the IGBT switching devices in an inverter. During the operation, IGBT power modules will experience high mechanical and thermal stresses which lead to bond wire lift-off and solder joint fatigue faults. Theses stresses can lead to malfunctions of the IGBT power modules. A short-circuit or open-circuit in any of the power modules may result in an instantaneous loss of traction power, which is dangerous for the driver and other road users. These reliability issues are very complex in their nature and demand for the development of analytical models and experimental validation. This work is set out to develop an online measurement technique for health monitoring of IGBT and freewheeling diodes inside the power modules. The technique can provide an early warning prior to a power device failure. Bond wire lift-off and solder fatigue are the two most frequently occurred faults in power electronic modules. The former increases the forward voltage drop across the terminals of the power device while the latter increase the thermal resistance of the solder layers. As a result, bond wire lift-off can be detected by a highly sensitive and fast operating in-situ monitoring circuit. Solder joint fatigue is detected by measuring the thermal impedance of the power modules. This thesis focuses on the design and optimisation of the in-situ health monitoring circuit in an attempt to reducing noise, temperature variations and measurement uncertainties. Experimental work is carried out on a set of various IGBT power modules that have been modified to account for different testing requirements. Then the lifetime of the power module can be estimated on this basis. The proposed health monitoring system can be integrated into the existing IGBT driver circuits and can also be applied to other applications such as industrial drives, aerospace and renewable energy.EThOS - Electronic Theses Online ServiceORSSchool of EEEGBUnited Kingdo

In-situ health monitoring of IGBT power modules in EV applications

Power electronics are an enabling technology and play a critical role in the establishment of an environmentally-friendly and sustainable low carbon economy. The electrification of passenger vehicles is one way of achieving this goal. It is well acknowledged that Electric vehicles (EVs) have inherent advantages over the conventional internal combustion engine (ICE) vehicles owing to the absence of emissions, high efficiency, and quiet and smooth operation. Over the last 20 years, EVs have improved significantly in their system integration, dynamic performance and cost. It has attracted much attention in research communities as well as in the market. In 2011 electric vehicle sales were estimated to reach about 20,000 units worldwide, increasing to more than 500,000 units by 2015 and 1.3 million by 2020 which accounts for 1.8 per cent of the total number of passenger vehicles expected to be sold that year. In general, electric vehicles use electric motors for traction drive, power converters for energy transfer and control, and batteries, fuel cells, ultracapacitors, or flywheels for energy storage. These are the core elements of the electric power drive train and thus are desired to provide high reliability over the lifetime of the vehicle. One of the vulnerable components in an electric power drive train is the IGBT switching devices in an inverter. During the operation, IGBT power modules will experience high mechanical and thermal stresses which lead to bond wire lift-off and solder joint fatigue faults. Theses stresses can lead to malfunctions of the IGBT power modules. A short-circuit or open-circuit in any of the power modules may result in an instantaneous loss of traction power, which is dangerous for the driver and other road users. These reliability issues are very complex in their nature and demand for the development of analytical models and experimental validation. This work is set out to develop an online measurement technique for health monitoring of IGBT and freewheeling diodes inside the power modules. The technique can provide an early warning prior to a power device failure. Bond wire lift-off and solder fatigue are the two most frequently occurred faults in power electronic modules. The former increases the forward voltage drop across the terminals of the power device while the latter increase the thermal resistance of the solder layers. As a result, bond wire lift-off can be detected by a highly sensitive and fast operating in-situ monitoring circuit. Solder joint fatigue is detected by measuring the thermal impedance of the power modules. This thesis focuses on the design and optimisation of the in-situ health monitoring circuit in an attempt to reducing noise, temperature variations and measurement uncertainties. Experimental work is carried out on a set of various IGBT power modules that have been modified to account for different testing requirements. Then the lifetime of the power module can be estimated on this basis. The proposed health monitoring system can be integrated into the existing IGBT driver circuits and can also be applied to other applications such as industrial drives, aerospace and renewable energy.EThOS - Electronic Theses Online ServiceORSSchool of EEEGBUnited Kingdo

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Data-driven prognostics for critical electronic assemblies and electromechanical components

The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

Non-invasive, innovative and promising strategy for breast cancer diagnosis based on metabolomic profile of urine, cancer cell lines and tissue

The work presented in this thesis aimed to establish the metabolomic profile of urine and breast cancer (BC) tissue from BC patients (samples cordially provided by Funchal Hospital), in addition to BC cell lines (MCF-7, MDA-MB-231, T-47D) as a powerful strategy to identify metabolites as potential BC biomarkers, helping on the development of non-invasive approaches for BC diagnosis and management. To achieve the main goal and obtain a deeper and comprehensive knowledge on BC metabolome, different analytical platforms, namely headspace solid-phase microextraction (HSSPME) combined with gas chromatography-quadrupole mass spectrometry (GC-qMS) and nuclear magnetic ressonance (1H NMR) spectroscopy were used. The application of multivariate statistical methods - principal component analysis (PCA) and orthogonal partial least square – discriminant analysis (OPLS-DA), to data matrix obtained from the different target samples allowed to find a set of highly sensitive and specific metabolites metabolites, namely, 4-heptanone, acetic acid and glutamine, able to be used as potential biomarkers in BC diagnosis. Significant group separation was observed in OPLS-DA score plot between BC and CTL indicating intrinsic metabolic alterations in each group. To attest the robustness of the model, a random permutation test with 1000 permutations was performed with OPLS-DA. The permutation test yielded R2 (represents goodness of fit) and Q2 values (represents predictive ability) with values higher than 0.717 and 0.691, respectively. Several metabolic pathways were dysregulated in BC considering the analytical approaches used. The main pathways included pyruvate, glutamine and sulfur metabolisms, indicating that there might be an association between the metabolites arising from the type of biological sample of the same donor used to perform the investigation. The integration of data obtained from different analytical platforms (GC-qMS and 1H NMR) for urinary and tissue samples revealed that five metabolites (e.g., acetone, 3-hexanone, 4-heptanone, 2methyl-5-(methylthio)-furan and acetate), were found significant using a dual analytical approach.O trabalho apresentado nesta tese teve como objetivo estabelecer o perfil metabolómico da urina e do tecido da mama de doentes com cancro de mama (BC) (amostras cordialmente fornecidas pelo Hospital do Funchal), além das linhas celulares de BC (MCF-7, MDA-MB-231, T -47D) como uma poderosa estratégia para identificar metabolitos como potenciais biomarcadores de BC, auxiliando no desenvolvimento de abordagens não invasivas para o diagnóstico e a gestão da patologia. Para obter um conhecimento mais profundo e abrangente do metaboloma de BC, diferentes plataformas analíticas, nomeadamente a microextração em fase sólida em modo headspace (HS-SPME) combinada com a cromatografia em fase gasosa acoplada à espectrometria de massa (GC-qMS) e espectroscopia de ressonância magnética nuclear (1H RMN), foram usadas para atingir o objetivo principal. A aplicação de métodos estatísticos multivariados - análise de componentes principais (PCA) e análise discriminante de mínimos quadrados parciais ortogonais (OPLS-DA) à matriz de dados obtida a partir das diferentes amostras alvo, permitiu estabelecer um grupo de metabolitos sensíveis e específicos, nomeadamente a 4-heptanona, o ácido acético e a glutamina, possíveis de serem utilizados como potenciais biomarcadores no diagnóstico de BC. Uma separação significativa entre os grupos BC e CTL foi observada pelo OPLS-DA, indicando alterações metabólicas em cada grupo. Para verificar a robustez do modelo, foi realizado um teste de permutação aleatória com 1000 permutações com o sistema OPLS-DA. Valores de R2 (representa o ajuste) e Q2 (representa a capacidade preditiva) superiores a 0,717 e 0,691, foram obtidos utilizando o teste da permutação. Diversas vias metabólicas estavam desreguladas no BC considerando as abordagens analíticas utilizadas. As principais vias incluíram os metabolismos do piruvato e glutamina, indicando que poderá haver uma associação entre os metabolitos derivados do tipo de amostra biológica do mesmo doador utilizado para realizar a investigação. A integração de dados obtidos pelas diferentes plataformas analíticas (GC-qMS e 1H RMN) para amostras urinárias e de tecido revelou cinco metabolitos significativos usando a dupla abordagem analítica. (i.e., acetona, 3-hexanona, 4-heptanona, 2-metil-5- (metiltio) - furano e acetato)