Paulo Rogério Scalassara

Bearing fault detection using relative entropy of wavelet components and artificial neural networks

Fault detection in electrical machines have been widely explored by researchers, especially bearing faults that represents about 40% to 60% of the total faults. Since this kind of fault is detectable by particular frequencies at the stator current, it is now a source of investigation. Thus, this work presents a predicability analysis method based on relative entropy measures estimated over reconstructed signals obtained from wavelet-packet decomposition components. The signals were simulated using a real motor current signal with addition of frequency components related to the bearing faults. Using three ANN topologies, these entropy measures are classified in two groups: normal and faulty signals with a high performance rate.

Induction motor fault diagnosis using wavelets and coordinate transformations

In this work, we presented a methodology for extraction of patterns for failure classification of three-phase induction machines from their line currents. The signals were acquired from machines under four different conditions: normal operation, stator winding short circuit, rotor broken bars, and bearings faults. The method consists of projecting the signals into the dq axes by use of the Clarke and Park Transforms. The oscillations of these signals, that are due to power quality problems and the faulty conditions, are quantified by the standard deviation of the details components of a wavelet decomposition using several levels. The proposed patterns have visible differences for the machines faulty conditions. Thus, in order to test them, we used a Kohonen Self-Organized Map network to try to classify them obtaining high classification accuracy for machines working with 25% of their nominal torque.

Power allocation scheme for mitigation of fiber temperature fluctuations in OCDMA networks based on firefly algorithm

Abstract In this paper, a power allocation scheme based on an evolutionary heuristic approach, namely Firefly Algorithm (FA) is proposed for the mitigation of fiber temperature fluctuations effects in the optical code division multiplexing access (OCDMA) networks. The temperature fluctuations degrade the 2-D wavelength-hopping time spreading optical codes by inducing a distortion on the autocorrelation of the received signal. The fiber temperature fluctuations are ordinarily hard to accurately determine and compensate due to the dynamic nature of the environmental temperature variation. In this context, power allocation (PA) policies constitute an efficient way to dynamically mitigate the effects of temperature variation with low cost and complexity of implementation. In this work, an FA input parameter optimization procedure has been conducted aiming to guarantee an efficient FA-based OCDMA power allocation algorithm regarding convergence velocity and quality of the solutions trade-off. The numerical results have demonstrated the effectiveness of the proposed FA power allocation scheme in mitigating the effects of fiber temperature fluctuations, as well as balancing the near–far effect (NFE) and multiple access interference (MAI). Moreover, the influence of the code parameters and the number of the nodes on the FA-based OCDMA power allocation scheme has been investigated. The comparison from both evolutionary heuristics FA and particle swarm optimization (PSO) power allocation schemes has indicated the faster convergence of FA-based scheme when the number of nodes K increases, while for both the complexity is similar, resulting in polynomial order O ( K 2 ) . Moreover, the FA-based power allocation approach presents a smoother and monotonic convergence when compared with the more oscillatory behavior of the PSO-based power allocation procedure.

Análise de fadiga muscular de sinais EMG com wavelet

Ha uma necessidade de estudar formas de analise para sinais EMG, ja que este metodo nao constitui formas invasivas de captacao de sinais musculares. Este trabalho tem como foco principal a explicacao do funcionamento da analise de sinais EMG utilizando a Transformada Wavelet Continua, sendo esta apresentada como uma combinacao linear de uma mother Wavelet e seus respectivos coeficientes. Toda esta analise sera efetuada para que seja possivel verificar os valores da frequencia media do musculo, dividindo o sinal em pequenos pedacos no tempo para a averiguacao do decaimento da frequencia media, indicando assim, a fadiga muscular.

Voice analysis of patients with neurological disorders using acoustical and nonlinear tools

In this paper, we analyze voice signals recorded from patients with neurological disorders of different etiologies. The study was based on three samples of each patient: one before any ingestion, one after the swallowing of a liquid solution, and one after the swallowing of a pasty solution. We used three approaches: first, acoustical analysis, specifically fundamental frequency, jitter and shimmer; second, a proposed analysis method of vocal dynamic visual patterns, which are based on phase space reconstruction of the signals; and third, relative entropy analysis between the groups of signals. We show that the acoustical measures were not able to differentiate the study cases, relative entropy was only partially able to perform this task, but the visual patterns analysis was successful.