Higher-order cumulants and spectral kurtosis for early detection of subterranean termites

Rosa, Juan José González de la; Moreno-Munoz, A.

doi:10.1016/j.ymssp.2007.08.009

Cited by 41 publications

(28 citation statements)

References 11 publications

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“…The spectral kurtosis, indicating the presence of short transients and their frequency locations, is obtained by calculating the kurtosis from each frequency band. For a number of -point realization at the frequency index , the spectral kurtosis is given as [23]…”

Section: Frequency Domain Featuresmentioning

confidence: 99%

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Guo

Gao

Huang

et al. 2016

Shock and Vibration

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Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

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Section: Frequency Domain Featuresmentioning

confidence: 99%

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Guo

Gao

Huang

et al. 2016

Shock and Vibration

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“…Higher-order cumulants are used to infer new properties about the data of non-Gaussian processes [9]. In multiple-signal processing it is very common to define the combinational relationship among the cumulants of r stochastic signals,…”

Section: Higher-order Statisticsmentioning

confidence: 99%

“…We remark the advances in [8], where two types of transients are distinguished via HOS, and [9] associated to PQ and plague detection. We remark the advances in [8], where two types of transients are distinguished via HOS, and [9] associated to PQ and plague detection.…”

Section: Introductionmentioning

confidence: 99%

HOS-Based Virtual Instrument for Power Quality Assessment

Sierra-Fernández

Rosa

Palomares-Salas

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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This paper describes a virtual instrument for PQ assessment. Conceived to detect transients, sags and swells, the computational nucleus is based in higher-order statistics, which enhance the statistical characterization of the raw data, with the consequent improvement of the instrument's performance. This measurement application is thought to be used by the plant operator previously trained in PQ analysis. The normal operation limit is previously established on empirical expert knowledge. The instrument also allows online visualization of the statistical parameters (variance, skewness and kurtosis). The repeatability is 85% roughly. Results convey the idea of an easy transferable technology, due to its low cost and optimized computational nucleus.

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“…HOS have been applied to many applications such as in 1D pattern recognition [9], chaotic signal characterization [10], array signal processing [11], telecommunication [12], ultrasound image processing [13], detection of mines from sonar images [14], study of machine faults [15], speaker verification [16], recognition of viruses from electron microscopic images [17], termite detection [18], analysis of bio signals like the ECG [19] and identification of cardiac ischemia [20]. Rama et al in his work has identified the difference between cardiac ischemia and the normal heart rate beat.…”

Section: Introductionmentioning

confidence: 99%

Analysis of cardiac beats using higher order spectra

Karaye

Saminu

Özkurt

2014

2014 IEEE 6th International Conference on Adaptive Science &Amp; Technology (ICAST)

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For early diagnosis of the heart failures, the electrocardiography (ECG) is the most common method because of its simplicity and cost. Computer based analysis of ECG provides reliable and efficient tools in diagnostics of arrhythmias. With this objective there are lots of studies on automatic and semi-automatic ECG analysis. Like many biosignals, ECG signals are nonlinear in nature, higher order spectral analysis (HOS) is known to be a very good tool for the analysis of nonlinear systems producing good noise immunity. Thus in this study, HOS analysis of ECG signals of normal heart rate, right bundle branch block, paced beat, left bundle block branch and atrial premature beats have been studied in order to reveal the complex dynamics of ECG signals using the tools of nonlinear systems theory. Some of the general characteristics for each of these classes in the bispectrum and bicoherence plot for visual observation have been presented. For the extraction of R-R intervals, well known Pan-Tompkins algorithm has been used and three higher order statistical parameters of skewness, kurtosis and variance from these features have been computed. These features with statistical parameters fed into artificial neural network classifier (ANN) and obtained an average accuracy of 94.9%.

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Higher-order cumulants and spectral kurtosis for early detection of subterranean termites

Cited by 41 publications

References 11 publications

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

HOS-Based Virtual Instrument for Power Quality Assessment

Analysis of cardiac beats using higher order spectra

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