Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Vulnerabilidade ambiental da zona costeira de Pititinga, Rio do Fogo, Rio Grande do Norte

This work presents a study on the environmental vulnerability of the coastal region of Pititinga, Rio do Fogo/RN. The coastal erosion of Pititinga beach was analyzed and considerated as one more process that produces environmental vulnerability in the area of study, taking into account its human and natural environment and establishing the relation between them, to understand the arrangement that produced its spatial configuration. The natural environment was expressed by thematics maps with geology, geomorphology, vegetation and soil themes, while the human environment was expressed by the use and occupation of the soil map. The coastal erosion was put in an erosion vulnerability map. The methodological procedure to generate the thematics maps, vulnerability maps and of the erosion coastal involved the bibliographic research, field visits with check-list form fill, collect and analysis of sediment sample, photo-interpretation techniques, integration of the information in a database, data store and spatial analysis in a Geographic Information System (GIS) ambient. The natural vulnerability map shows a predominancy of environments with low (29,6%) or medium (42,4%) vulnerability, pointed the frontal and mobile dune as the areas with the highest vulnerability. The environmental vulnerability map, presents a predominancy of environments with low vulnerability (53,8%), with the high vulnerability concentrated on Pititinga community. The coastal erosion vulnerability presented distinct behaviors on three sections among the coastal line according each one characteristics. Where there are frontal and transgressive dunes, vulnerability are, generally, medium or low, respectively, and in the absence of them, as what occurs in Pititinga community, the vulnerability is predominately very high