Weak transient signal detection based on continuous wavelet and cumulant analysis

Abstract

Based on the continuous wavelet transform and the 4" order cumulant analysis as well as a threshold derived from the NCynm-PeZSOn criterion, this paper presents a simple and effective algorithm for detection of weak transient signals severely contaminated by high level noise. The proposed method is shown to offer a sigiiicantly hetter detection probability, especially in poor sigual-to-noise ratio scenarios, via comparison with other methods hased 011 the continuous wavelet transform only and based on the short-time Fourier transform and the Wipner-Ville distrihution with or without cumulant analysis.

A Literature Review Research on Monitoring Conditions of Mechanical Equipment Based on Edge Computing

“…In this article, we apply a pre-processing method based on edge computing to monitor the signal process of equipment status and extract the information of fault features through a short-time Fourier transform or wavelet transform [ 16 ], such as time domain characteristics of faults, frequency domain characteristics of faults and fault characteristics of the time–frequency domain, so that the fault information can be diagnosed quickly. However, not all fault information can be obtained by this method, such as identification based on high and low resonance components [ 17 ] and weak transient signal processing [ 18 ].…”

“…In this article, we apply a pre-processing method based on edge computing to monitor the signal process of equipment status and extract the information of fault features through a shorttime Fourier transform or wavelet transform [16], such as time domain characteristics of faults, frequency domain characteristics of faults and fault characteristics of the time-frequency domain, so that the fault information can be diagnosed quickly. However, not all fault information can be obtained by this method, such as identification based on high and low resonance components [17] and weak transient signal processing [18]. When signal data needs complex calculation, such as pulse signal, decay signal, noise signal, and other combined signal components, the data is transmitted to the cloud for analysis and calculation, therefore, we need to compress and transmit them, and decompose the signal and identify the fault characteristics in the cloud for such data types.…”

Non‐cooperative burst detection and synchronisation in downlink TDMA‐based wireless communication networks

“…Transient signals are generally considered as short duration signals compared to the observation time. The aim of transient signal detector is to decide whether the observation consists of noise alone or the signal is embedded in noise [25–27]. Various detection schemes have been proposed for this purpose.…”

“…When the transient signal is partially known, an often used procedure is the likelihood ratio test (LRT). When the signal to be detected is unknown, energy‐based detectors become the only choice [25]. In [26], the performance of transient detection based on higher order moments is investigated and a detector based on the third‐order absolute moment is proposed.…”

Improved method for detecting weak abrupt information based on permutation entropy

“…There are many kinds of methods for detecting abrupt information, such as Fourier transform, wavelet analysis, singular value decomposition, higher-order cumulant, detrended fluctuation analysis, and approximate entropy. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] Fourier transform could only determine the overall nature of the abrupt information, which was difficult to determine the exact time position of the abrupt point embedded in signal. Wavelet analysis could meet the requirements for detection of signals with multiple frequencies.…”