Application of non-stationary signal characteristics using wavelet packet transformation

Abstract

Although Fourier-based methods have been standard methods for frequency analysis, they are not well suited for the analysis of nonlinear or non-stationary systems due to their time-varying natures. Thus, in this paper, a wavelet packetbased technique, which calculates time-varying coherence functions for input/output relationships, is developed. The developed method uses the Coiflet wavelet that has been widely used in signal processing. It is applied to obtain the time-varying coherence function, and to detect the impulse signal from the impulse-embedded signal such as an automobile sound/vibration signal with an external impact caused by a collision or passing over rough terrain. Some characteristics of non-stationary behavior such as the wavelet packet coefficients, maximum phase plane (MPP) analysis and fault detection are also demonstrated. The method gives promising results of non-stationary input-output systems, and so may be used as an effective tool for condition monitoring or fault detection area.

Impeller fault detection under variable flow conditions based on three feature extraction methods and artificial neural networks

“…Pump rotational frequency, vane passing frequency [11], and the corresponding harmonics are proposed as indicators that can provide valuable information regarding a centrifugal pump impeller that can be strongly affected by variable conditions. By contrast, wavelet analysis, which simultaneously performs timefrequency analysis (TFA), is an effective approach for dealing with nonstationary signals [12,13] and an effective tool for rapidly exhibiting variable amplitude and phase changes [14]. Wavelet analysis expands signals in terms of wavelets produced through the scaling and translation of a mother wavelet.…”

Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

“…Wavelet packet analysis stems from wavelet analysis, compared with wavelet analysis, wavelet packet analysis can decompose frequency band to multi-level, at the same time, it can further decompose low frequency part and high frequency part of signal, and it also can self-adapt to choose of the corresponding frequency band according to the characteristics of signals, to match its the single spectrum so as to improves the time-frequency resolution. Wavelet packet analysis is a better signal analysis method for non-stationary signal [9,10].…”

The Adaptability Problem Analysis on Structural Response Calculation with Wideband Signals Excitation

“…Then how to solve the problems about broadband excitation dynamics? Here we propose a digital signal processing method that we can decompose the broadband excitation signal into narrowband excitation signal with the signal decomposition and then come again to calculate respectively [6,7,8]. For a linear system, the broadband excitation signal ) (t Q can be broken down as high frequency excitation signal ) (t Q H , intermediate frequency signal ) (t Q M and the low frequency signal ) (t Q L by using the method of signal decomposition.…”