The importance of recognizing the presence of process faults and resolving these faults is continuously increasing parallel to the development of industrial processes. Fault detection methods which are both robust and sensitive help to recognize the presence of faults in time to avoid malfunctions, financial loss, environmental damage or loss of human life. In the literature, the use of various model-based fault detection methods has gained a considerable degree of popularity. Methods usually based on black-box models, data-based techniques or models using symbolic logic, e.g.\ expert systems, have become widespread. White-box models, on the other hand, have been applied less despite their considerable robustness because of multiple reasons. Firstly, their complexity and the relatively vast amount of technological and modelling knowledge needed to construct them for industrial systems. Secondly, their large computational demand which makes them less suitable for online fault detection. In this study, the aim was to resolve these problems by developing a method to simplify the complex Computational Fluid Dynamics models employed to describe the equipment used in the chemical industry into less complex model structures. These simpler structures are Compartment Models, a type of white-box model which breaks down a complex system into smaller units with idealized behaviour. In the case of a small number of compartments, the computational load of such models is not significant, therefore, they can be employed for the purposes of online fault detection while providing an accurate representation of the system. For the purpose of identifying the compartmental structure, fuzzy logic was employed to create a model which approximates the real behaviour of the system as accurately as possible. Our future objective is to explore the possibility of combining this model with various diagnostic methods (expert systems, Bayesian networks, parity relations, etc.) and derive robust tools for the purpose of fault detection.
Waterbirds depend on a dispersed network of wetlands for their annual life cycle during migration. Climate and land use changes raise new concerns about the sustainability of these habitat networks, as water scarcity triggers ecological and socioeconomic impacts threatening wetland availability and quality. During the migration period, birds can be present in large enough numbers to influence water quality themselves linking them and water management in efforts to conserve habitats for endangered populations. Despite this, the guidelines within laws do not properly account for the annual change of water quality due to natural factors such as the migration periods of birds. Principal component analysis and principal component regression was used to analyze the correlations between the presence of a multitude of migratory waterbird communities and water quality metrics based on a dataset collected over four years in the Dumbrăvița section of the Homoród stream in Transylvania. The results reveal a correlation between the presence and numbers of various bird species and the seasonal changes in water quality. Piscivorous birds tended to increase the phosphorus load, herbivorous waterbirds the nitrogen load, while benthivorous duck species influenced a variety of parameters. The established PCR water quality prediction model showed accurate prediction capabilities for the water quality index of the observed region. For the tested data set, the method provided an R2 value of 0.81 and a mean squared prediction error of 0.17.
Fault Detection and Isolation (FDI) methodology focuses on maintaining safe and reliable operating conditions within industrial practices which is of crucial importance for the profitability of technologies. In this work, the development of an FDI algorithm based on the use of dynamic principal component analysis (DPCA) and the Fréchet distance δdF metric is explored. The three-tank benchmark problem is studied and utilized to demonstrate the performance of the FDI method for six fault types. A DPCA transformation for the system was established, and fault detection was conducted based on the Q statistic. Fault isolation is also of critical importance for proper intervention to mitigate fault effects. To identify the type of detected faults, the fault responses within the PC subspace were analyzed using the δdF metric. The use of the Fréchet distance metric for the isolation of faults combined with DPCA for feature extraction is a novel technique to the best of the authors’ knowledge that provides a robust computational tool with low computational cost for FDI purposes that fits well into the Industry 4.0 framework.The robustness and sensitivity of the method was validated for a wide variety of signal-to-noise ratio (SNR) conditions, with findings indicating a possible average false and missed alarm rate of 0.1 and a macro-averaged F-score above 0.8 in all cases.
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