Harmonic wavelet transform

About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.

Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current’s FFT

Abstract: The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time–frequency behavior of the current’s components associated to fault conditions Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase’s current The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors’ knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 315-MW induction motor with broken bars

Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings:

Abstract: Signal processing plays a pivotal role in fault diagnostics of mechanical systems. An approach, viz. wavelet transform-based higher-order statistics, was developed in this paper for fault diagnosis in rolling element bearings. In the approach, wavelet transform (discrete wavelet and wavelet packet transform) was introduced into a fourth-order statistic, kurtosis. Thereinto, discrete wavelet transform-based kurtosis (DWTK) was applied to signals to get a higher resolution in low-frequency bands1 on the other hand, wavelet packet transform-based kurtosis (WPTK) was applied to obtain a relatively high resolution in high-frequency bands in comparison with the DWTK. DWTK, WPTK and wavelet transform-based kurtosis (WTK) curves were introduced to calibrate the in-field signals in comparison with the benchmark signals, whereby the non-stationary transients and singularity in the vibration signals attributed to damage were detected. WTK curves of vibration signals collected from bearing with damage of different se...

Sparse signal representations using the tunable Q-factor wavelet transform

Abstract: The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented using radix-2 FFTs. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform.

Signal reconstruction from the short-time Fourier transform magnitude

Abstract: This paper presents various conditions that are sufficient for reconstructing a discrete-time signal from samples of its short-time Fourier transform magnitude. For applications such as speech processing, these conditions place very mild restrictions on the signal as well as the analysis window of the transform. Examples of such reconstruction for speech signals are included in the paper.