The theoretical analysis for an iterative envelope algorithm

Yang, Lijun; Yang, Zhihua

doi:10.1016/j.dsp.2014.12.006

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…So it can be concluded that the method presented in this paper improves classification accuracy obviously. Figure 18 shows the accuracy of feature extraction and SVM recognition proposed in this paper by the eight methods mentioned in Table 1, which are CS (Cubic Spline), CTCP (Cubic Trigonometric Cardinal Precise spline [24]), BS (B-Spline [22]), OS (Direct constrained optimization [28]), OPCH (Optimized Piecewise Cubic Hermite [23]), MPCI (Monotone Piecewise Cubic Interpolation [29]), PPFA (Piecewise Power Function Algorithm [30]). By comparison, it is found that the comprehensive accuracy of this algorithm is superior to other algorithms.…”

Section: Methodsmentioning

confidence: 99%

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

Liu

Qin

Liu

2019

Sensors

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For compound fault detection of high-speed rail vibration signals, which presents a difficult problem, an early fault diagnosis method of an improved empirical mode decomposition (EMD) algorithm based on quartic C2 Hermite interpolation is presented. First, the quartic C2 Hermite interpolation improved EMD algorithm is used to decompose the original signal, and the intrinsic mode function (IMF) components are obtained. Second, singular value decomposition for the IMF components is performed to determine the principal components of the signal. Then, the signal is reconstructed and the kurtosis and approximate entropy values are calculated as the eigenvalues of fault diagnosis. Finally, fault diagnosis is executed based on the support vector machine (SVM). This method is applied for the fault diagnosis of high-speed rails, and experimental results show that the method presented in this paper is superior to the traditional EMD algorithm and greatly improves the accuracy of fault diagnosis.

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Section: Methodsmentioning

confidence: 99%

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

Liu

Qin

Liu

2019

Sensors

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“…Despite its importance, there is no precise, general mathematical definition for the temporal envelope of a signal in the digital signal processing (DSP) literature [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

Signal-Envelope: A C++ library with Python bindings for temporal envelope estimation

Tarjano¹,

Pereira²

2021

Preprint

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Signals can be interpreted as composed of a rapidly varying component modulated by a slower varying envelope. Identifying this envelope is an essential operation in signal processing, with applications in areas ranging from seismology to medicine. Conventional envelope detection approaches based on classic methods tend to lack generality, however, and need to be tailored to each specific application in order to yield reasonable results. Taking inspiration from geometric concepts, most notably the theory of alpha-shapes, we introduce a general-purpose library to efficiently extract the envelope of arbitrary signals.

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Envelope estimation using geometric properties of a discrete real signal

Santos

Pereira²

2022

Digital Signal Processing

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The theoretical analysis for an iterative envelope algorithm

Cited by 3 publications

References 14 publications

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

Signal-Envelope: A C++ library with Python bindings for temporal envelope estimation

Envelope estimation using geometric properties of a discrete real signal

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