A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

Rosa, Juan José González de la; Agüera-Pérez, Agustín; Palomares-Salas, José Carlos; Fernández, José María Sierra

doi:10.1016/j.measurement.2015.02.044

Cited by 26 publications

(18 citation statements)

References 11 publications

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“…A variety of defect types coexist in a log, but in this study, defect types identified were mainly decay, void, crack, or other structural defects. Also, because large SK values located in some FBs were caused by the dominant components (associated with defects), while small SK values were induced by noise, and thus the small SK can be omitted for more effective analysis (Antoni and Randall 2006;De la Rosa et al 2015). The FBs with the larger SK values were extracted and contrasted to the actual defects from the cutting-open log one by one to determine the corresponding relationship between them.…”

Section: Sk Based Defect Type Identification 421 Complex Morlet Wavmentioning

confidence: 99%

“…can determine whether defects exist in a log but not identify the defect types for the fact that no discrimination being made among the frequency bands (FBs) of the acoustic signal (Xu et al 2018(Xu et al , 2019. The application of spectral kurtosis (SK) makes possible for solving the problem (De la Rosa and Moreno Muñoz 2008;De la Rosa et al 2015). In essence, SK is to calculate the kurtosis at each frequency line for revealing the hidden nonstationary components in signal and determine in which FBs they occur.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

Liu

Brashaw

et al. 2020

Holzforschung

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The aim of this research was to explore the potential of acoustic impact test to evaluate the condition of hardwood logs in regard to internal decay, void, crack and defect ratio using an acoustic signal separation and enhancement algorithm. Longitudinal acoustic signals were obtained from 15 logs of four hardwood species through acoustic impact testing. The defect components were separated from the acoustic response signals and enhanced based on the autoregressive minimum entropy deconvolution (AR-MED) method, and from which the kurtosis was derived and used as the global feature parameter for evaluating the internal condition of logs. Compared with the acoustic velocity obtained directly from the original signal, the kurtosis was deemed to be a more powerful predictor of log defect ratio with higher coefficient of determination (R2 = 0.89) and was not affected by log species. To identify the type of defects, a complex Morlet wavelet-based spectral kurtosis (SK) method was proposed. The research results indicated that the SK can not only determine the type and primary and secondary major defects, but also be able to identify those that were not detectable by global acoustic parameters.

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Section: Sk Based Defect Type Identification 421 Complex Morlet Wavmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

Liu

Brashaw

et al. 2020

Holzforschung

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“…Antoni [ 11 , 12 ] put forward the method of spectral kurtosis (or SK) for non-stationary feature extraction. Due to its superiority, several in-depth studies have been done in the bearing vibration monitoring and fault diagnosis [ 13 , 14 , 15 , 16 ]. Based on short-time Fourier Transform (STFT), Antoni [ 17 ] later built a faster computation method for kurtogram to extract transient features in vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

Chen¹,

Feng²,

Zhang³

2016

Sensors

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Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

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“…Detection methods based on acoustic technologies have been proposed in the literature [15,16,17,18,19,20,21], including ultrasonic ones [22], and have been patented, but are unreliable due to external interferences, such as human behavior or structural vibrations, involving excessive false positives. Acoustic and piezoelectric techniques are adequate for some species, especially for scrapping, but it is nearly impossible to design an efficient autonomous electronic device due to the high energy requirements in the recording and processing of insect signals.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low Power Optical Sensor for Xylophagous Insect Detection in Wood

Perles

Mercado

Capella

et al. 2016

Sensors

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The early detection of pests is key for the maintenance of high-value masterpieces and historical buildings made of wood. In this work, we the present detailed design of an ultra-low power sensor device that permits the continuous monitoring of the presence of termites and other xylophagous insects. The operating principle of the sensor is based on the variations of reflected light induced by the presence of termites, and specific processing algorithms that deal with the behavior of the electronics and the natural ageing of components. With a typical CR2032 lithium battery, the device lasts more than nine years, and is ideal for incorporation in more complex monitoring systems where maintenance tasks should be minimized.

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A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

Cited by 26 publications

References 11 publications

Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

Ultra-Low Power Optical Sensor for Xylophagous Insect Detection in Wood

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