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 th...
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| Muut tekijät: | |
| Aineistotyyppi: | doctoralThesis |
| Kieli: | por |
| Julkaistu: |
Universidade Federal do Rio Grande do Norte
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| Aiheet: | |
| Linkit: | https://repositorio.ufrn.br/jspui/handle/123456789/18577 |
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| Yhteenveto: | 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 |
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