Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on time series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto-and cross-correlations for every variable. After that, a time-series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.

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This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness.

The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered by the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyse large areas and find new paths providing maximum visibility. This paper shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called Visibility-based Path Planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas from the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.

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