The influence of the wavelet filter in the parameters extraction for signal classification: An experimental study.

Varanis, Marcus; Pederiva, Robson

doi:10.5540/03.2017.005.01.0501

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…In the signal processing, the Meyer (mey) filter was used for the CWT, while for the wavelet packet the discrete Meyer (DMEY) was applied. The use of these filters instead of others is due the fact that they posses a phase near to linear and a high support, and they have a great frequency response, that is very close to the ideal one (Varanis and Pederiva, 2017, 2015).…”

Section: Resultsmentioning

confidence: 99%

Remarks on the Sommerfeld effect characterization in the wavelet domain

Varanis

Balthazar

Silva

et al. 2018

Journal of Vibration and Control

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In many applications in engineering, a mechanical system operates in the nonstationary regime, either partially of fully, creating the possibility to generate nonlinearities in them. Great efforts have been made to better understand and characterize these phenomena. One of several methods that are being used for the processing of signals of a nonstationary nature, as well as for the characterization of nonlinearities in mechanical systems, is the wavelet transform. A particular phenomenon that is seen in systems operating in the nonstationary regime is the Sommerfeld effect, which occurs due to the nonlinear interaction between a nonideal energy source and a mechanical system. This phenomenon can lead to high amplitudes of vibration for the system that in turn can cause damage in it. Therefore, this work presents an application of the continuous wavelet transform and the wavelet packet transform for the characterization of the Sommerfeld effect in mechanical systems where only the time response is at hand. Experimental procedures were performed where a nonideal energy source (an unbalanced DC motor) was used to excite (a) a portal frame and (b) a three-story shear-building. The results showed the effectiveness and the potential of the methods proposed.

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Section: Resultsmentioning

confidence: 99%

Remarks on the Sommerfeld effect characterization in the wavelet domain

Varanis

Balthazar

Silva

et al. 2018

Journal of Vibration and Control

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“…Unlike discrete wavelet transform, the filtering operations in wavelet packet transform, are also applied to the wavelet, or detail coefficients. The frequency-localised filter; Fejer-Korovkin wavelet has been shown to be a good approximation [38]. The result is that wavelet packets provide a sub-band filtering of the input signal into progressively finer equal-width intervals.…”

Section: Feature Extraction Of Pd Signals Using Wptmentioning

confidence: 99%

Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method

Iorkyase

Tachtatzis

Glover

et al. 2019

IEEE Trans. Power Delivery

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Partial discharge (PD) is regarded as a precursor to plant failure and therefore an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency based technique uses the Wavelet Packet Transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The Regression Tree algorithm, Bootstrap Aggregating method and Regression Random Forest (RRF) are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-Random Forest PD localization scheme produced superior performance as a result of its robustness against noise.

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