Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis

Muñoz, Carlos Quiterio Gómez; Jiménez, Alfredo; Márquez, Fausto Pedro García

doi:10.1016/j.renene.2017.03.052

Cited by 77 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The WPT method has been widely applied to decompose signals and extract characteristic features [43][44][45]. The WPT method was used to extract features from on-line cutting force signals for the real-time monitoring of surface roughness in automated manufacturing systems [43].…”

Section: Health Indicators Extractionmentioning

confidence: 99%

“…The WPT method was used to extract features from on-line cutting force signals for the real-time monitoring of surface roughness in automated manufacturing systems [43]. In Reference [45], the WPT combined with the manifold learning method was used to extract the weak signals for the rolling bearing fault diagnosis. In Reference [45], the wavelets transform method was employed to filter and diagnosis the ice condition on the wind turbine blade surface.…”

Section: Health Indicators Extractionmentioning

confidence: 99%

“…In Reference [45], the WPT combined with the manifold learning method was used to extract the weak signals for the rolling bearing fault diagnosis. In Reference [45], the wavelets transform method was employed to filter and diagnosis the ice condition on the wind turbine blade surface. In this paper, the WPT method is used to de-noise and extract the developed health indicators.…”

Section: Health Indicators Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

et al. 2018

View full text Add to dashboard Cite

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

show abstract

Section: Health Indicators Extractionmentioning

confidence: 99%

Section: Health Indicators Extractionmentioning

confidence: 99%

Section: Health Indicators Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

et al. 2018

View full text Add to dashboard Cite

show abstract

“…us, there are many time-frequency analysis methods proposed that are based on Fourier analysis: shorttime Fourier transform (STFT), Wigner-Ville distributions, Cohen class, S transform, wavelet transform (WT), and so on [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Testing Analysis of Concrete Structure Based on S Transform

Wei²

2019

Shock and Vibration

View full text Add to dashboard Cite

Ultrasonic testing analysis is a crucial scientific component during the process and interpretation of the ultrasonic detection signal. Focusing on the ultrasonic testing characteristics, the time-invariant spectral analysis method cannot meet the processing requirements of the detection signal fully. Thus, S transform, the time-varying analysis method, was introduced into the ultrasonic testing data processing of the concrete structure. The acoustic wave phase velocity was derived based on the spectrum analysis, and the S transform time-frequency analysis method was established. Finally, based on the method of concrete experimental data set, studies show that the frequency energy spectrum with S transform can realize flexible and effective identification of defects in the concrete structure. Definitely, this analysis method can significantly improve the resolution and practicality of ultrasonic testing.

show abstract

“…Time-frequency analysis, such as wavelet transform (WT) and wavelet packet transform (WPT), is able to reveal their frequency components more accurately than frequency analysis like fast Fourier transform (FFT) [35]. It also helps to suppress the influence of noise whose energy is generally in low frequency scales [36]. The frequency components of ultrasonic stress waves generally have diverse sensitivities to various structural defects, and can be used to identify defects more precisely than the waveforms in the time domain [37].…”

Section: Introductionmentioning

confidence: 99%

Grouting Quality Evaluation in Post-Tensioning Tendon Ducts Using Wavelet Packet Transform and Bayes Classifier

Sun

et al. 2019

Sensors

View full text Add to dashboard Cite

The grouting quality of tendon ducts is very important for post-tensioning technology in order to protect the prestressing reinforcement from environmental corrosion and to make a smooth stress distribution. Unfortunately, various grouting defects occur in practice, and there is no efficient method to evaluate grouting compactness yet. In this study, a method based on wavelet packet transform (WPT) and Bayes classifier was proposed to evaluate grouting conditions using stress waves generated and received by piezoelectric transducers. Six typical grouting conditions with both partial grouting and cavity defects of different dimensions were experimentally investigated. The WPT was applied to explore the energy of received stress waves at multi-scales. After that, the Bayes classifier was employed to identify the grouting conditions, by taking the traditionally used total energy and the proposed energy vector of WPT components as input, respectively. The experimental results demonstrated that the Bayes classifier input with the energy vector could identify different grouting conditions more accurately. The proposed method has the potential to be applied at key spots of post-tensioning tendon ducts in practice.

show abstract

Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis

Cited by 77 publications

References 33 publications

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Ultrasonic Testing Analysis of Concrete Structure Based on S Transform

Grouting Quality Evaluation in Post-Tensioning Tendon Ducts Using Wavelet Packet Transform and Bayes Classifier

Contact Info

Product

Resources

About