9 research outputs found

    An artificial neural network-based diagnostic methodology for gas turbine path analysis—part I: introduction

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    The reliability of gas path components (compressor, burners and turbines) of a gas turbine is generally high, when compared with those of other systems. However, in case of forced stops, downtime is usually high, with a relatively low availability. The purpose of condition monitoring and fault diagnostics is to detect, isolate and evaluate (i.e., to estimate quantitatively the extent) defects within a system. One effective technique could provide a significant improvement of economic performance, reduce operating costs and maintenance, increase the availability and improve the level of safety achieved. However, conventional analytical techniques such as gas path analysis and its variants are limited in their engine diagnostic, due to several reasons, including their inability to effectively operate in the presence of noise measures, to distinguish anomalies of component from a failure sensor, to preserve the linearity in the relations between parameters of gas turbines and to manage the sensors range to achieve accurate diagnosis. In this paper, the approach of a diagnostic scenario to detect faults in the gas path of a gas turbine has been presented. The model provides a largescale integration of artificial neural networks designed to detect, isolate and evaluate failures during the operating conditions. The engine measurements are considered as input for the model, such as the speed, pressure, temperature and fuel flow rate. The output supplies any changes in the sensor or in the efficiency levels and flow rate, in the event of fault components. The diagnostic method has the ability to evaluate anomalies of both multiple components and multiple sensors, within the range of operating points. In the case of components failures, the system provides diagnostic changes in efficiency and flow rate that can be interpreted to determine the nature of the physical problem. The technique has been applied in different operating conditions by comparing the results obtained with the solutions provided by linear and nonlinear analysis

    Prevention of work-related airway allergies; summary of the advice from the Health Council of the Netherlands

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    The Health Council of the Netherlands published a report in which the best procedure and method for recommending health-based occupational exposure limits (OELs) for inhaled allergens were identified by evaluating the scientific state of the art. Many respiratory disorders in the workplace arise from inhalation of substances which can cause allergy. To protect workers against respiratory allergy, various preventive measures are taken, one of them being reduction of exposure by setting legally binding standards. These are based on health-based OELs that specify a level of exposure to an airborne substance, a threshold level, below which it may reasonably be expected that there is no risk of adverse health effects. The Council is of the opinion that an OEL should prevent against allergic sensitization, as sensitization plays a crucial biological role and is a prerequisite for the development of allergy. Furthermore, the Council considers it most likely that the exposure level below which no allergic sensitization develops for most allergens is so low, that OELs are difficult to set with the current knowledge and technical feasibilities. An alternative approach is to accept exposure, which carries a small predefined risk in developing allergic sensitization. In addition, it is worth considering periodic screening of exposed workers on allergic sensitization, because timely intervention can prevent worse. The feasibility of periodic screening and what else is needed to comply with the most important criteria, should however be judged case-by-case
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