Non-Stationary Signals: Phase-Energy Approach—theory and Simulations

Klein, Raphael; Ingman, Dov; Braun, Sebastian

doi:10.1006/mssp.2001.1398

Cited by 35 publications

(12 citation statements)

References 11 publications

(8 reference statements)

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“…This paper proposes to adopt the particle swarm optimization (PSO) algorithm [13] to optimize the model parameters of DBN for machine condition assessment, which can reduce the complexity of DNN modeling. Nowadays, in addition to time and frequency analysis and other traditional methods, some novel analysis techniques such as short time Fourier transform (STFT) [14], Wigner Ville Distribution (WVD) [15], and wavelet packet transform (WPT) [16] have also been effectively applied to extract vibration signal features. Feature fusion reduction is the process to produce new and more sensitive features through a series of transformations or combinations of the original input feature sets.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

et al. 2018

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This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

et al. 2018

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“…Contrarily, time-frequency features can present a synthetic consideration for mechanical fault detection by characterizing varying frequency information at different times. Commonly used timefrequency analysis methods include short-time Fourier transform (Klein et al 2001), Wigner-Ville distribution (Baydar and Ball 2001), wavelet transform (WT) (Wang et al 2011) and empirical mode decomposition (Peng et al 2005;Rai and Mohanty 2007). Among these techniques, WT is outstanding in rotary machine diagnosis because its multi-resolution merit is suitable for analyzing signals with transient impulses.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

2015

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This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective features that can discriminate different fault conditions. Based on the sparse TFD, a new sparse wavelet energy (SWE) feature is obtained by three main steps: first, an overcomplete discrete DWT is employed to decompose the fault signal and construct a redundant dictionary; second, the redundant dictionary is optimized by basis pursuit to obtain the sparsest TFD; finally, SWE is calculated from the new TFD to produce a feature vector for each signal. SWE features that combine the merits of overcomplete DWT and sparse representation techniques can precisely reveal fault-induced information, thereby exhibiting valuable properties for automatic fault identification by intelligent classifiers. The effectiveness and advantages of the proposed features are confirmed by simulation and the practical fault pattern recognition of rolling bearings.

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“…Thus, it is not an easy task to detect the transients with the machine health information in the low signal-to-noise ratio conditions. A number of methods, such as time-frequency analysis [20][21][22], wavelet transform (WT) [23][24][25][26], sparse decomposition [27][28][29], empirical mode decomposition (EMD) [30][31][32], spectral kurtosis (SK) [33][34][35][36][37], and manifold learning [38][39][40], are proposed to extract the feature of vibration signals, especially the transients, for rotating machine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Wang

Cai

Zhu

et al. 2015

Mechanical Systems and Signal Processing

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Non-Stationary Signals: Phase-Energy Approach—theory and Simulations

Cited by 35 publications

References 11 publications

RETRACTED: Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

RETRACTED: Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

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