A review on empirical mode decomposition in fault diagnosis of rotating machinery

Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

Application of the EEMD method to rotor fault diagnosis of rotating machinery

Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

Rotating machine fault diagnosis using empirical mode decomposition

Matching pursuit based on nonparametric waveform estimation

Based on that higher-order cumulants is insensitive to add-Gaussian noise and symmetric noise, a method for detecting feature of signal embedded in additive noise is presented, which combines a filter banks and higher-order cumulants, neither the supposition of the observed signal is Gaussian or stationary, nor a prior information about the waveform and arrive time of the observed signal is necessary. The primary principle of the proposed method is to separate the spectrum of the observed signal into narrow frequency band by using a bandpass filter banks, those subfilters which have large output energies are chosen to give a time-frequency description of the observed signal approximately, each chosen subfilter is then followed by a short-time estimation of third-order cumulant so that to suppress colored noise and detect the signal feature. Simulated and experimental results show that the method is effective and practical, even if the signal feature is submerged into noise signal completely.

Feature Detection Based on Filter Banks and Higher Order Cumulants

A technique for on-line quality detection of ultrasonic wire bonding is developed The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality The method includes three steps First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic Second, each subband envelope is obtained using the Hilbert transform for further feature extraction Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes The different waveform features are extracted from each phase of these subband envelopes The principal components analysis method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables Using the selected features as inputs, an artificial neural network is constructed to identify the complex bond fault pattern By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality

On-Line Quality Detection of Ultrasonic Wire Bonding via Refining Analysis of Electrical Signal From Ultrasonic Generator

This paper presents a novel method for extracting feature waveforms of the compound signal, which combined the nonparametric waveform estimation based on filter bank with the conventional matching pursuit (MP) At each iteration of the MP, the proposed method expressed the extracted feature waveform that has the best matched with the signal local structure with a series expansion of a set of nonparametric basis functions which generated by filtering the template signal through a bandpass filter bank A prior signal information is no more require for a template signal owe to apply the adaptive template signal in this method So, the construction of general described by some parameters is unnecessary and it felicitates the method for a wide variety of applications Simulated and experimental results verify the practicability and effectiveness of this algorithm, even if the noise frequency band coincides with that of the feature waveform of the signal

Qingfeng Meng

Papers

Rotating machine fault diagnosis using empirical mode decomposition

Matching pursuit based on nonparametric waveform estimation

Feature Detection Based on Filter Banks and Higher Order Cumulants

On-Line Quality Detection of Ultrasonic Wire Bonding via Refining Analysis of Electrical Signal From Ultrasonic Generator

Feature Extraction Based on Nonparametric Waveform Estimation and Matching Pursuit