Navegando por Assunto "Diagnóstico de faltas"

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Acesso aberto (Open Access)
Estudos de estratégias de identificação paramétrica para detecção e diagnóstico de faltas em um processo industrial do tipo tanques comunicantes
(Universidade Federal do Pará, 2012-04-22) SILVA, Raphael Diego Comesanha e; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
This dissertation presents a technique for detection and diagnosis of incipient faults, which cause changes in behavior of the system under investigation and are reflected in the mathematical model’s parameters values variation. As a testbed, was constructed a model of an industrial system computing environment Matlab/Simulink, which consists of a dynamic plant composed of two tanks linked to each other. The modeling of this plant was carried out by physical equations that describe the dynamics of the system. The fault, which the system was submitted, represents a gradual clogging in the exit pipe of the tank 2. This bottleneck causes a gradual reduction, up to 20%, of the pipe section. The technique of fault detection was performed by real-time estimation of parameters Auto-regressive models with exogenous inputs (ARX) with fuzzy and Recursive Least Squares (RLS) estimators. Already, the percentage clogging diagnosis of the pipe was obtained by a fuzzy system parameter tracking, fed back by the integral of the residue detection. Using this methodology, it was possible to detect and diagnose the simulated fault in three differents operating points of the system. In both techniques tested, the RLS method perform well, only to detect fault. Otherwise, the fuzzy method performed better, in detect and diagnose the fault applied to the system, noting the work propose.
Acesso aberto (Open Access)
Metodologia baseada em sistema fuzzy intervalar do tipo-2 para detecção e identificação de faltas de incipientes em motores de indução
(Universidade Federal do Pará, 2013-02-27) ROCHA, Erick Melo; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Since the incorporation of automation in the production processes, aiming at order to improve productivity and quality of products and services, researches on more efficient methodologies for fault diagnosis became more intensive. Such techniques allow the early detection of faults, before then lead to failures. This work investigates techniques for detection and diagnosis of faults and its application to induction motors, limiting their study to two situations, namely: system free of faults and system under incipient partial short-circuit in the coils the stator winding. For faults detection, parametric analysis of fist order ARX (autoregressive with exogenous input) were applied. The parameters of identified ARX modes, which bring information about the dynamics of the dominant system, are recursively obtained by the techniques of recursive least squares (RLS). In order to evaluate the capability for early fault detection, a type-2 interval fuzzy system was developed. This kind of fuzzy system has capability to capture a larger set of uncertainties than conventional (type-1) fuzzy systems. The footprint of uncertainty (FOU), characteristic of type-2 fuzzy system, is a way to accounts for uncertainties coming from noise and numerical errors from the process of parameter estimation. The ARX model parameters are the inputs to the supervisor system. Genetic algorithms (GA’s) were used for optimization of SIF interval type-2, aiming at to reduce the diagnostic error. The results obtained in tests of computer simulation show the effectiveness of the proposed methodology.