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Many econometric models have been constructed based on various theoretical approaches (neoclassical economics, Keynesian, business cycle, post Keynesian and others) aiming to predict peaks and pitfalls in the economies. But unfortunately many of them were misleading in predicting 2007-2008 financial crisis and long lasting recession afterwards. The literature indicates plenty of negative aspects of recent financial crisis at macro-level including decrease in consumption, aggregate demand and investment, financial markets' stagnation, increase in Government debt and unemployment rate and even more political instability. While effects at micro level encompass the decline in business loans, fall in equity value, property devaluation, decrease in sales, increase in defaults, decrease in investment in R&D and etc. Especially small open economies, like Lithuania, have suffered most in terms of shrinkage of GDP during the 2007-2009 period. Therefore the qualitative approach is valuable in deepening the knowledge about the preconditions and consequences of credit shocks. Thus this study aims to extract the main prerequisites and outcomes for credit shocks and to approve it by the expert opinion. Expert evaluation results indicate that absence of reserve funds was the most important precondition for 2007-2008 crisis and recession. Moreover when the gathered unplanned cyclical income have been wasted and even more the public sector spending has been increased that led to even bigger budget deficit. Also experts unanimously identified the main consequences of credit shock and 2007-2009 crisis including: (1) Decrease in GDP and government revenues, (2) Fall in domestic demand in Lithuania and increase in importance of exports for economic development, (3) The decline in consumption level and the delay of purchase, and (4) Shrinkage of construction sector, and manufacturing and trade of durable goods.

Many econometric models have been constructed based on various theoretical approaches (neoclassical economics, Keynesian, business cycle, post Keynesian and others) aiming to predict peaks and pitfalls in the economies. But unfortunately many of them were misleading in predicting 2007-2008 financial crisis and long lasting recession afterwards. The literature indicates plenty of negative aspects of recent financial crisis at macro-level including decrease in consumption, aggregate demand and investment, financial markets' stagnation, increase in Government debt and unemployment rate and even more political instability. While effects at micro level encompass the decline in business loans, fall in equity value, property devaluation, decrease in sales, increase in defaults, decrease in investment in R&D and etc. Especially small open economies, like Lithuania, have suffered most in terms of shrinkage of GDP during the 2007-2009 period. Therefore the qualitative approach is valuable in deepening the knowledge about the preconditions and consequences of credit shocks. Thus this study aims to extract the main prerequisites and outcomes for credit shocks and to approve it by the expert opinion. Expert evaluation results indicate that absence of reserve funds was the most important precondition for 2007-2008 crisis and recession. Moreover when the gathered unplanned cyclical income have been wasted and even more the public sector spending has been increased that led to even bigger budget deficit. Also experts unanimously identified the main consequences of credit shock and 2007-2009 crisis including: (1) Decrease in GDP and government revenues, (2) Fall in domestic demand in Lithuania and increase in importance of exports for economic development, (3) The decline in consumption level and the delay of purchase, and (4) Shrinkage of construction sector, and manufacturing and trade of durable goods.DOI: http://dx.doi.org/10.5755/j01.ee.27.1.9533

Many econometric models have been constructed based on various theoretical approaches (neoclassical economics, Keynesian, business cycle, post Keynesian and others) aiming to predict peaks and pitfalls in the economies. But unfortunately many of them were misleading in predicting 2007-2008 financial crisis and long lasting recession afterwards. The literature indicates plenty of negative aspects of recent financial crisis at macro-level including decrease in consumption, aggregate demand and investment, financial markets' stagnation, increase in Government debt and unemployment rate and even more political instability. While effects at micro level encompass the decline in business loans, fall in equity value, property devaluation, decrease in sales, increase in defaults, decrease in investment in R&D and etc. Especially small open economies, like Lithuania, have suffered most in terms of shrinkage of GDP during the 2007-2009 period. Therefore the qualitative approach is valuable in deepening the knowledge about the preconditions and consequences of credit shocks. Thus this study aims to extract the main prerequisites and outcomes for credit shocks and to approve it by the expert opinion. Expert evaluation results indicate that absence of reserve funds was the most important precondition for 2007-2008 crisis and recession. Moreover when the gathered unplanned cyclical income have been wasted and even more the public sector spending has been increased that led to even bigger budget deficit. Also experts unanimously identified the main consequences of credit shock and 2007-2009 crisis including: (1) Decrease in GDP and government revenues, (2) Fall in domestic demand in Lithuania and increase in importance of exports for economic development, (3) The decline in consumption level and the delay of purchase, and (4) Shrinkage of construction sector, and manufacturing and trade of durable goods.

Many econometric models have been constructed based on various theoretical approaches (neoclassical economics, Keynesian, business cycle, post Keynesian and others) aiming to predict peaks and pitfalls in the economies. But unfortunately many of them were misleading in predicting 2007-2008 financial crisis and long lasting recession afterwards. The literature indicates plenty of negative aspects of recent financial crisis at macro-level including decrease in consumption, aggregate demand and investment, financial markets' stagnation, increase in Government debt and unemployment rate and even more political instability. While effects at micro level encompass the decline in business loans, fall in equity value, property devaluation, decrease in sales, increase in defaults, decrease in investment in R&D and etc. Especially small open economies, like Lithuania, have suffered most in terms of shrinkage of GDP during the 2007-2009 period. Therefore the qualitative approach is valuable in deepening the knowledge about the preconditions and consequences of credit shocks. Thus this study aims to extract the main prerequisites and outcomes for credit shocks and to approve it by the expert opinion. Expert evaluation results indicate that absence of reserve funds was the most important precondition for 2007-2008 crisis and recession. Moreover when the gathered unplanned cyclical income have been wasted and even more the public sector spending has been increased that led to even bigger budget deficit. Also experts unanimously identified the main consequences of credit shock and 2007-2009 crisis including: (1) Decrease in GDP and government revenues, (2) Fall in domestic demand in Lithuania and increase in importance of exports for economic development, (3) The decline in consumption level and the delay of purchase, and (4) Shrinkage of construction sector, and manufacturing and trade of durable goods.

Many econometric models have been constructed based on various theoretical approaches (neoclassical economics, Keynesian, business cycle, post Keynesian and others) aiming to predict peaks and pitfalls in the economies. But unfortunately many of them were misleading in predicting 2007-2008 financial crisis and long lasting recession afterwards. The literature indicates plenty of negative aspects of recent financial crisis at macro-level including decrease in consumption, aggregate demand and investment, financial markets' stagnation, increase in Government debt and unemployment rate and even more political instability. While effects at micro level encompass the decline in business loans, fall in equity value, property devaluation, decrease in sales, increase in defaults, decrease in investment in R&D and etc. Especially small open economies, like Lithuania, have suffered most in terms of shrinkage of GDP during the 2007-2009 period. Therefore the qualitative approach is valuable in deepening the knowledge about the preconditions and consequences of credit shocks. Thus this study aims to extract the main prerequisites and outcomes for credit shocks and to approve it by the expert opinion. Expert evaluation results indicate that absence of reserve funds was the most important precondition for 2007-2008 crisis and recession. Moreover when the gathered unplanned cyclical income have been wasted and even more the public sector spending has been increased that led to even bigger budget deficit. Also experts unanimously identified the main consequences of credit shock and 2007-2009 crisis including: (1) Decrease in GDP and government revenues, (2) Fall in domestic demand in Lithuania and increase in importance of exports for economic development, (3) The decline in consumption level and the delay of purchase, and (4) Shrinkage of construction sector, and manufacturing and trade of durable goods.

The Autonomic Logistics System Simulation (ALSim) model was developed to provide decision makers a tool to make informed decisions regarding the Joint Strike Fighter's (JSF) Autonomic Logistics System (ALS). The ALS provides real-time maintenance information to ground maintenance crews, supply depots, and air planners to efficiently manage the availability of JSF aircraft. This thesis effort focuses on developing a methodology to model the Prognostics and Health Management (PHM) component of ALS. The PHM component of JSF monitors the aircraft status. To develop a PHM methodology to use in ALSim a neural network approach is used. Notional JSF prognostic signals were generated using an interactive Java application, which were then used to build and train a neural network. The neural network is trained to predict when a component is healthy and/or failing. The results of the neural network analysis are meaningful failure detection times and false alarm rates. The analysis presents a batching approach to train the neural network, and looks at the sensitivity of the results to batch size and the neural network classification rule used. The final element of the research is implementing the PHM methodology in the (ALSim).

International audience ; In this article, we used Wireless Sensor Network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead to coverage hole and incomplete data arriving to the sink. Therefore, preserving the energy held by the nodes so that the network keeps running for as long as possible is a very important concern. If we achieve to improve the network lifetime and Quality of Service (QoS). Diagnosing the state of area will be more accurate for a longer time. One of the most important elements to achieve a QoS in WSN is the network coverage which is usually interpreted as how well the network can observe a given area. Obviously, if the coverage decreases over time, the diagnosis quality decreases accordingly. Various coverage strategies are thus proposed by the WSN community, in order to guarantee a certain coverage rate as long as possible, to reach a certain QoS that in turn will impact the diagnosis and prognostic quality. Various other strategies are in common use in WSN like data aggregation and scheduling, to preserve a QoS in wireless sensor networks, as long as possible. We argue that such strategies are not neutral if this network is used for prognostic and health management. Some politics may have a positive impact while other ones may blur the sensed data, like data aggregation or redundancy suppression, leading to erroneous diagnostics and/or prognostics. In this work, we will show and measure the impact of each WSN strategy on the resulting estimation of diagnostics. We emphasized several issues and studied various parameters related to these strategies that have a very important impact on the network, ...

International audience ; In this article, we used Wireless Sensor Network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead to coverage hole and incomplete data arriving to the sink. Therefore, preserving the energy held by the nodes so that the network keeps running for as long as possible is a very important concern. If we achieve to improve the network lifetime and Quality of Service (QoS). Diagnosing the state of area will be more accurate for a longer time. One of the most important elements to achieve a QoS in WSN is the network coverage which is usually interpreted as how well the network can observe a given area. Obviously, if the coverage decreases over time, the diagnosis quality decreases accordingly. Various coverage strategies are thus proposed by the WSN community, in order to guarantee a certain coverage rate as long as possible, to reach a certain QoS that in turn will impact the diagnosis and prognostic quality. Various other strategies are in common use in WSN like data aggregation and scheduling, to preserve a QoS in wireless sensor networks, as long as possible. We argue that such strategies are not neutral if this network is used for prognostic and health management. Some politics may have a positive impact while other ones may blur the sensed data, like data aggregation or redundancy suppression, leading to erroneous diagnostics and/or prognostics. In this work, we will show and measure the impact of each WSN strategy on the resulting estimation of diagnostics. We emphasized several issues and studied various parameters related to these strategies that have a very important impact on the network, and therefore on data diagnostics over time. To reach this goal, to evaluate both prognostic and health management with the WSN strategies, we have used six diagnostic algorithms.

The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure.

International audience ; The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure.

International audience ; The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure.