Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Desenvolvimento de um sistema de proteção e metodologia para localização de faltas em sistemas de distribuição

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Alocação eficiente de indicadores de faltas em um sistema de distribuição real usando computação evolutiva

Este trabalho propõe uma abordagem computacional evolutiva para a resolução do problema de alocação de dispositivos indicadores de faltas (IFs) em alimentadores primários de distribuição de energia elétrica. De forma mais específica, o problema de se obter o melhor local de instalação é solucionado por meio da técnica de Algoritmos Genéticos (AGs) que busca obter uma configuração eficiente de instalação de IFs no tronco principal de um alimentador de distribuição. Assim, faz-se a modelagem do mesmo na forma de um problema de otimização orientado à melhoria dos indicadores de qualidade do serviço e ao encontro de uma solução economicamente atraente. Os resultados com dados reais comprovam a eficiência da metodologia proposta.

Locating the Seattle Fault in Bellevue, WA: Combining geomorphic indicators with borehole data

The Seattle Fault is an active east-west trending reverse fault zone that intersects both Seattle and Bellevue, two highly populated cities in Washington. Rupture along strands of the fault poses a serious threat to infrastructure and thousands of people in the region. Precise locations of fault strands are still poorly constrained in Bellevue due to blind thrusting, urban development, and/or erosion. Seismic reflection and aeromagnetic surveys have shed light on structural geometries of the fault zone in bedrock. However, the fault displaces both bedrock and unconsolidated Quaternary deposits, and seismic data are poor indicators of the locations of fault strands within the unconsolidated strata. Fortunately, evidence of past fault strand ruptures may also be recorded indirectly by fluvial processes and should also be observable in the subsurface. I analyzed hillslope and river geomorphology using LiDAR data and ArcGIS to locate surface fault traces and then compare/correlate these findings to subsurface offsets identified using borehole data. Geotechnical borings were used to locate one fault offset and provide input to a cross section of the fault constructed using Rockworks software. Knickpoints, which may correlate to fault rupture, were found upstream of this newly identified fault offset as well as upstream of a previously known fault segment.

Hydraulic Subsurface Measurements and Hydrodynamic Modeling as Indicators for Groundwater Flow Systems in the Rotondo Granite, Central Alps (Switzerland)

Regional groundwater flow in high mountainous terrain is governed by a multitude of factors such as geology, topography, recharge conditions, structural elements such as fracturation and regional fault zones as well as man-made underground structures. By means of a numerical groundwater flow model, we consider the impact of deep underground tunnels and of an idealized major fault zone on the groundwater flow systems within the fractured Rotondo granite. The position of the free groundwater table as response to the above subsurface structures and, in particular, with regard to the influence of spatial distributed groundwater recharge rates is addressed. The model results show significant unsaturated zones below the mountain ridges in the study area with a thickness of up to several hundred metres. The subsurface galleries are shown to have a strong effect on the head distribution in the model domain, causing locally a reversal of natural head gradients. With respect to the position of the catchment areas to the tunnel and the corresponding type of recharge source for the tunnel inflows (i.e. glaciers or recent precipitation), as well as water table elevation, the influence of spatial distributed recharge rates is compared to uniform recharge rates. Water table elevations below the well exposed high-relief mountain ridges are observed to be more sensitive to changes in groundwater recharge rates and permeability than below ridges with less topographic relief. In the conceptual framework of the numerical simulations, the model fault zone has less influence on the groundwater table position, but more importantly acts as fast flow path for recharge from glaciated areas towards the subsurface galleries. This is in agreement with a previous study, where the imprint of glacial recharge was observed in the environmental isotope composition of groundwater sampled in the subsurface galleries. Copyright © 2012 John Wiley & Sons, Ltd.

Brucite Microbialites in Living Coral Skeletons: Indicators of Extreme Microenvironments in Shallow-Marine Settings

Brucite [Mg(OH)2] microbialites occur in vacated interseptal spaces of living scleractinian coral colonies (Acropora, Pocillopora, Porites) from subtidal and intertidal settings in the Great Barrier Reef, Australia, and subtidal Montastraea from the Florida Keys, United States. Brucite encrusts microbial filaments of endobionts (i.e., fungi, green algae, cyanobacteria) growing under organic biofilms; the brucite distribution is patchy both within interseptal spaces and within coralla. Although brucite is undersaturated in seawater, its precipitation was apparently induced in the corals by lowered pCO2 and increased pH within microenvironments protected by microbial biofilms. The occurrence of brucite in shallow-marine settings highlights the importance of microenvironments in the formation and early diagenesis of marine carbonates. Significantly, the brucite precipitates discovered in microenvironments in these corals show that early diagenetic products do not necessarily reflect ambient seawater chemistry. Errors in environmental interpretation may arise where unidentified precipitates occur in microenvironments in skeletal carbonates that are subsequently utilized as geochemical seawater proxies.