Wavelet-based detection of abrupt changes in natural frequencies of time-variant systems

Dziedziech, Kajetan; Staszewski, Wiesław J.; Basu, Biswajit; Uhl, Tadeusz

doi:10.1016/j.ymssp.2015.03.012

Cited by 21 publications

(16 citation statements)

References 34 publications

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“…This concept of multiresolution analysis is provided by the wavelet transform. Unlike Fourier transforms, wavelets can capture discontinuities and sharp spikes more efficiently due to wavelet functions' finite duration nature [35]. Capturing such information is significant in the differentiation between sounds.…”

Section: Feature Extractionmentioning

confidence: 99%

Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Al-Quayed

Soudani

Al-Ahmadi

2020

J Wireless Com Network

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Wireless acoustic sensor networks represent an attractive solution that can be deployed for animal detection and recognition in a monitored area. A typical configuration for this application would be to transmit the whole acquired audio signal through multi-hop communication to a remote server for recognition. However, continuous data streaming can cause a severe decline in the energy of the sensors, which consequently reduces the network lifetime and questions the viability of the application. An efficient solution to reduce the sensor's radio activity would be to perform the recognition task at the source sensor then to communicate the result to the remote server. This approach is intended to save the energy of the acoustic source sensor and to unload the network from carrying, probably, useless data. However, the validity of this solution depends on the energy efficiency of performing on-sensor detection of a new acoustic event and accurate recognition. In this context, this paper proposes a new scheme for on-sensor energy-efficient acoustic animal recognition based on low-complexity methods for feature extraction using the Haar wavelet transform. This scheme achieves more than 86% in recognition accuracy while saving 71.59% of the sensor energy compared with the transmission of the raw signal.

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Section: Feature Extractionmentioning

confidence: 99%

Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Al-Quayed

Soudani

Al-Ahmadi

2020

J Wireless Com Network

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“…It is well known that in engineering applications the scaling parameter a can be related directly to frequency. Following the previous work reported in [22][23][24], the complex Morlet wavelet is used, which is composed of the complex exponential carrier and the Gaussian window as an envelope. This wavelet function can be defined as…”

Section: (A) Continuous Wavelet Transformmentioning

confidence: 99%

Wavelet-based analysis of time-variant adaptive structures

Dziedziech

Nowak

Hasse

et al. 2018

Phil. Trans. R. Soc. A.

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Wavelet analysis is applied to identify the time-variant dynamics of adaptive structures. The wavelet-based power spectrum of the structural response, wavelet-based frequency response function (FRF) and wavelet-based coherence are used to identify continuously and abruptly varying natural frequencies. A cantilever plate with surface-bonded macro fibre composite-which alters the structural stiffness-is used to demonstrate the application of the methods. The results show that the wavelet-based input-output characteristics-i.e. the FRF and coherence-can identify correctly the dynamics of the analysed time-variant system and reveal the varying natural frequency. The wavelet-based coherence can be used not only for the assessment of the quality of the wavelet-based FRF but also for the identification.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

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“…Hou et al [16,17] proposed a continuous wavelet-based technique for identification of instantaneous modal parameters of time-varying structures. Dziedziech et al [18] used the wavelet-based frequency response function (FRF) for modal identification of time-variant systems. An identification approach based on the empirical wavelet transform (EWT) was developed for LTV systems by Xin et al [19].…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

Zhang

Shi

2019

Shock and Vibration

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Instantaneous modal parameter identification of time-varying dynamic systems is a useful but challenging task, especially in the identification of damping ratio. This paper presents a method for modal parameter identification of linear time-varying systems by combining adaptive time-frequency decomposition and signal energy analysis. In this framework, the adaptive linear chirplet transform is applied in time-frequency analysis of acceleration response for its higher energy concentration, and the response of each mode can be adaptively decomposed via an adaptive Kalman filter. Then, the damping ratio of the time-varying systems is identified based on energy analysis of component response signal. The proposed method can not only improve the accuracy of instantaneous frequency extraction but also ensure the antinoise ability in identifying the damping ratio. The efficiency of the method is first verified through a numerical simulation of a three-degree-of-freedom time-varying structure. Then, the method is demonstrated by comparing with the traditional wavelet and time-domain peak method. The identified results illustrate that the proposed method can obtain more accurate modal parameters in low signal-to-noise ratio (SNR) scenarios.

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Wavelet-based detection of abrupt changes in natural frequencies of time-variant systems

Cited by 21 publications

References 34 publications

Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

Wavelet-based analysis of time-variant adaptive structures

Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

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