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Fault detection and classification are considered as one of the most mandatory techniques in nowadays industrial monitoring. The necessity of fault monitoring is due to the fact that early detection can restrain high-cost maintenance. Due to the complexity of the wind turbines and the considerable amount of data available via SCADA systems, machine learning methods and specifically deep learning approaches seem to be powerful means to solve the problem of fault detection in wind turbines. In this article, a novel deep learning fault detection and classification method is presented based on the time-series analysis technique and convolutional neural networks (CNN) in order to deal with some classes of faults in wind turbine machines. To validate this approach, challenging scenarios, which consists of less than 5% performance reduction (which is hard to identify) in the two actuators or four sensors of the wind turbine along with sensors noise are investigated, and the appropriate structures of CNN are suggested. Finally, these algorithms are evaluated in simulation based on the data of a 4.8 MW wind turbine benchmark and their accuracy approves the convincing performance of the proposed methods. The proposed algorithm are applicable to both on-shore and off-shore wind turbine machines.

It is well-recognised that to achieve long-term sustainable and resilient land management we need to understand the coupled dynamics of social and ecological systems. Land use change scenarios will often aim to understand (i) the behaviours of land management, influenced by direct and indirect drivers, (ii) the resulting changes in land use and (iii) the environmental implications of these changes. While the literature in this field is extensive, approaches to parameterise coupled systems through integration of empirical social science based models and ecology based models still need further development. We propose an approach to land use dynamics modelling based on the integration of behavioural models derived from choice experiments and spatially explicit systems dynamics modelling. This involves the specification of a choice model to parameterise land use behaviour and the integration with a spatial habitat succession model. We test this approach in an upland socio-ecological system in the United Kingdom. We conduct a choice experiment with land managers in the Peak District National Park. The elicited preferences form the basis for a behavioural model, which is integrated with a habitat succession model to predict the landscape level vegetation impacts. The integrated model allows us to create projections of how land use may change in the future under different environmental and policy scenarios, and the impact this may have on landscape vegetation patterns. We illustrate this by showing future projection of landscape changes related to hypothetical changes to EU level agricultural management incentives. The advantages of this approach are (i) the approach takes into account potential environmental and management feedbacks, an aspect often ignored in choice modelling, (ii) the behavioural rules are revealed from actual and hypothetical choice data, which allow the research to test the empirical evidence for various determinants of choice, (iii) the behavioural choice models generate probabilities of alternative behaviours which make them ideally suited for integration with simulation models. The paper concludes that the modelling approach offers a promising route for linking socio-economic and ecological features of socio-ecological systems. Furthermore, our proposed approach allows testing of the underlying socio-economic and environmental drivers and their interaction in real environmental systems.