Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis

Qin, Yi; Xing, Jianfeng; Mao, Yongfang

doi:10.1088/0957-0233/27/8/085003

Cited by 60 publications

(37 citation statements)

References 24 publications

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“…For the complex wavelet function, the conventional analog implementation method is to construct the real part and imaginary part independently [6,40]. That means doubled components are required in the analog circuit.…”

Section: B Filter Construction By Common-pole Strategymentioning

confidence: 99%

“…4.31×10 5 3.38×10 2 4.03×10 8 2.65 SDR 1.98×10 6 1.01×10 3 1.21×10 9 7.94 TS 1.28×10 7 1.39×10 5 2.50×10 7 1.91×10 3…”

Section: Fdrunclassified

“…Morlet wavelet transform (MWT) has been recognized as an efficient tool in many areas of interest, including signal or image denoising [1,2], transients detection [3,4] and feature extraction [5,6]. Typically, MWT is implemented by digital signal processing methods [3,7,8], through which the signals are firstly converted into digital fashion, and then processed by digital signal processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

Wang¹,

Meng²,

Wang³

2020

IEEE Access

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In this paper, a novel methodology is proposed to implement the Morlet wavelet transform in an analog circuit. Under the proposed scheme, the impulse response of the linear time-invariant system is used to approximate the Morlet wavelet function. The approximation accuracy is guaranteed by the constrained L 2-norm method, which reduces the approximation error by decreasing free parameters in searching routines. Due to the complex wavelet function, a common-pole strategy is presented for filter construction. With the common poles in real and imaginary parts, partial components can be shared during the analog implementation of the wavelet transform. With the reasonably good approximation for Morlet wavelet function, an analog continuous-time filter is designed based on the Gm-C integrator and orthonormal ladder topology. Application examples are introduced to illustrate the superior performance of the proposed filter. It is shown that the designed analog filter approximates the ideal Morlet wavelet function well in both time and frequency.

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Section: B Filter Construction By Common-pole Strategymentioning

confidence: 99%

“…4.31×10 5 3.38×10 2 4.03×10 8 2.65 SDR 1.98×10 6 1.01×10 3 1.21×10 9 7.94 TS 1.28×10 7 1.39×10 5 2.50×10 7 1.91×10 3…”

Section: Fdrunclassified

Section: Introductionmentioning

confidence: 99%

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Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

Wang¹,

Meng²,

Wang³

2020

IEEE Access

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“…In order to obtain the position of the zero optical path difference of the white light interference signal, we analyzed the white light interference fringe pattern. Using both the seven-step phase shift method [39] and the Morlet wavelet transform method [40]. The seven-step phase shift method has a fast processing speed and better calculation effect but poor anti-interference ability.…”

Section: Proposed Approachmentioning

confidence: 99%

Measuring the Refractive Index and Its Two-Dimensional Distribution of Picoliter Microfluidics With the Scanning White Light Interference Microscopy Method

Xia

Zhan

et al. 2020

IEEE Access

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Measurements on refractive index and its distribution of microfluidics are widely applied in the field of biological detection, chemical analysis, material researches, medical diagnostics, and lab on a chip. Since the volume of the microfluidics is ultra-small, such as nanoliters or even picoliters, measuring the refractive index of microfluidics is challenging. We here present an effective method to measure the refractive index and its two-dimensional distribution of picoliter microfluidics by the SWLI (scanning white light interference) microscopy technology. This method has the advantages of simplicity, low cost, non-contact, wide measurement range and high precision. We conduct the experiment to measure the refractive index of 2.4 picoliters liquid. The resolution is 10-4 RIU (refractive index unit), and the relative error is 10-4 compared with the Abbe refractometer. We obtained the relationship between refractive index and concentration of glucose solution. In addition, we can measure the 2D (two-dimensional) distribution of RI (refractive index) of liquids. We can observe the liquid interface between deionized water and liquid paraffin clearly. Besides, we can observe the process of solute dissolution in the liquid and the change of refractive index.

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“…This approach is also simple and characterizes an intuitive nature by allotting the components to their corresponding frequency in a particular spectrum. Time-frequency domain approaches including wavelet analysis, the fast Fourier transform (FFT), Wigner-Ville distribution, and Hilbert-Huang transform, etc., investigate waveform signals in both the time and frequency domain and can provide more information about the data classification [43,44]. However, this approach is essentially more complicated than the frequency-domain or time-domain approaches in a practical scenario.…”

Section: Support Vector Machine For Data Classificationmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

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Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis

Cited by 60 publications

References 24 publications

Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

Measuring the Refractive Index and Its Two-Dimensional Distribution of Picoliter Microfluidics With the Scanning White Light Interference Microscopy Method

Internet of Things for Sensing: A Case Study in the Healthcare System

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