Nowadays, it is necessary to train professionals to address today's changing in the labor market and promote the competitive economy. As a result of using an interactive training tool, the sector s competitiveness as well as the staff s qualification would be improved through the promoting learning with Open Educational Resources (OER) and their access to the professional. Training the professionals will promote the competitive economy and will facilitate the mobility and professionalization of the personnel particularly in increasing sectors such as wind energy. The ICT tools are a valuable solution for the wind energy sector considering that wind farms are often located in remote areas and an ICT training tool would facilitate the staff s qualification from every place. Providing information and advices about the operational (installation and maintenance) skills will improve notably the qualification of the staff involved in the wind sector, promoting an innovative training to consolidate the sustainability of this sector specially now when in addition of new installed power a revamping of the existing wind farms is needed in many European countries. In parallel the tool being developed within this work would learn from the experience and from the best practices. Enhancing digital integration in training will improve the demands of the current market.

This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here, we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high-quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modeling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10 percent in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5 percent of that required when using the comparable Entropy-based method.

This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments' Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant's Key Performance Factors.