Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

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Este documento presenta una metodología para estimar los parámetros de un modelo de un horno de arco eléctrico usando regularización de Tikhonov. La regularización de Tikhonov es uno de los métodos de regularización usado más comúnmente. El modelo de horno de arco utilizado considera la naturaleza no lineal y altamente variable que exhibe este tipo de carga. Se ha utilizado el toolbox Regularization Tools desarrollado para Matlab que permite determinar el valor del vector de parámetros estimados de norma mínima.Los resultados obtenidos en simulación del modelo del horno de arco implementado en PSCAD son comparados con mediciones reales tomadas en la etapa más crítica de la operación del horno. Se muestra cómo el comportamiento del modelo del horno de arco con un ajuste apropiado de los parámetros, captura en un alto porcentaje las formas de onda de los voltajes trifásicos de fase en el secundario del transformador que energiza los electrodos; además, se obtienen corrientes de línea eficaces de arco eléctrico con errores no mayores al 2,8 % del valor real.