Improved discrete Fourier transform algorithm for harmonic analysis of rotor system

Yao, Jinbao; Tang, Baoping; Zhao, Jie

doi:10.1016/j.measurement.2016.01.028

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…The STFT has adopted its decomposition method from global to local, which is used on nonstationary signal processing; however, it shows inefficiency in multi-resolution analysis and nonlinearity conditions. The wavelet-based techniques have improved the loss of information and the resolution limitations introduced by Fourier analysis; however, desired wavelet requires a strict design of filters for the diverse fault types, and thus any improper selection of the basis function can affect the analysis results [33,47].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering

et al. 2019

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Gas pressure regulators are widely applied in natural gas pipeline networks, correspondingly, establishing an efficient fault diagnosis approach of regulators plays a critical role in optimizing the safety and reliability of pipeline network systems. In our paper, considering that the outlet pressure signals of gas regulators are nonstationary and nonlinear, we propose a fault diagnosis approach combining a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy c-means (FCM) clustering to classify three typical faults of gas regulators. First, we propose to apply the CEEMDAN approach for decomposing intrinsic mode functions (IMFs). Then feature vectors of the typical faults are established by Hilbert marginal spectrum (HMS) of IMFs. Finally, we adopt cluster centers and feature clustering algorithm to distinguish the types of faults. The experimental results indicate the high performance of the present fault diagnosis approach. The membership degrees of test samples obtained from the CEEMDAN algorithm are optimized to be within 0.9 to 1. INDEX TERMS Gas pressure regulators, fault diagnosis, CEEMDAN, feature extraction, spectral analysis, fuzzy c-means clustering.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering

et al. 2019

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“…Conventional representation learning methods are not capable of revealing frequency‐wise variations. Some researchers have adopted FFT to extract both amplitude‐wise dynamics and frequency‐wise variations . Wang et al utilized FFT to obtain the frequency signature of each fault of Cascaded‐Multilevel Inverter.…”

Section: Introductionmentioning

confidence: 99%

Amplitude‐frequency images‐based ConvNet: Applications of fault detection and diagnosis in chemical processes

Zhang

Wang

Gao

et al. 2019

Journal of Chemometrics

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Fault detection and diagnosis (FDD) have been major concerns in abnormal event management of chemical processes for decades. Frequency-wise variations in chemical processes are not considered in most traditional methods, which affects the monitoring performance. An amplitude-frequency imagesbased convolutional neural network (ConvNet) is proposed for FDD in chemical processes. The fast Fourier transform (FFT) is first performed on data slice collected within a period to extract both amplitude-wise dynamics and frequency-wise variations, with the results in images. Then, the amplitudefrequency images are fed into ConvNet for FDD. ConvNet is applied as a binary classifier, in which each classifier corresponds to only one fault. Thus, an expandable framework is provided to incorporate a new fault. The performance of the proposed amplitude-frequency images-based ConvNet in FDD is demonstrated in a numerical case and the Tennessee Eastman process. KEYWORDS amplitude-frequency image, chemical process, convolutional neural network, fast Fourier transform, fault detection and diagnosis

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“…However, almost all of the methods above have their own limitations. For example, wavelet analysis has the advantage of detecting the fast and frequent fluctuations of harmonics, but it is sensitive to noise [5]. Generally, for the wavelet transform, it is important to choose the appropriate wavelet basis function, which also brings about difficulties for signal analysis.…”

Section: Introductionmentioning

confidence: 99%

A Revised Hilbert–Huang Transform and Its Application to Fault Diagnosis in a Rotor System

Wang

2018

Sensors

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As a classical method to deal with nonlinear and nonstationary signals, the Hilbert–Huang transform (HHT) is widely used in various fields. In order to overcome the drawbacks of the Hilbert–Huang transform (such as end effects and mode mixing) during the process of empirical mode decomposition (EMD), a revised Hilbert–Huang transform is proposed in this article. A method called local linear extrapolation is introduced to suppress end effects, and the combination of adding a high-frequency sinusoidal signal to, and embedding a decorrelation operator in, the process of EMD is introduced to eliminate mode mixing. In addition, the correlation coefficients between the analyzed signal and the intrinsic mode functions (IMFs) are introduced to eliminate the undesired IMFs. Simulation results show that the improved HHT can effectively suppress end effects and mode mixing. To verify the effectiveness of the new HHT method with respect to fault diagnosis, the revised HHT is applied to analyze the vibration displacement signals in a rotor system collected under normal, rubbing, and misalignment conditions. The simulation and experimental results indicate that the revised HHT method is more reliable than the original with respect to fault diagnosis in a rotor system.

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Improved discrete Fourier transform algorithm for harmonic analysis of rotor system

Cited by 12 publications

References 21 publications

Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering

Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering

Amplitude‐frequency images‐based ConvNet: Applications of fault detection and diagnosis in chemical processes

A Revised Hilbert–Huang Transform and Its Application to Fault Diagnosis in a Rotor System

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