Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment

Kebabsa, Tarek; Djebala, Abderrazek; Khemissi, Babouri Mohamed; Cherif, D.; Chaabi, Lilia; Ouelaa, Nouredine

doi:10.1007/s00170-020-05848-z

Cited by 19 publications

(17 citation statements)

References 30 publications

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“…Yang_2017 [161] Isham_2019 [162] Amarnath_2013 [163] Mao_2018 [164] Chen_2015 [165] Rafiq_2021 [166] Isham_2018 [167] Jegadeeshwaran_2014 [168] Cyclostationary and cyclo-non-stationary analysis [173] Sun_2020 [174] Jeon_2020 [175] Fan_2020 [176] Youcef_2020 [177] Yang_2019 [178] Xin_2018 [179] Hamadache_2018 [180] Song_2018 [181] Golbaghi_2017 [182] Li_2016c [137] Raj_2015 [183] Ocak_2001 [184] Oh_2018 [185] Tarek_2020 [186] Li_2018 [187] Hong_2017 [188] Cerrada_2015 [189] Fan_2015 [190] Yang_2018 [191] Qiang_2014 [192] Moghadam_2021 [193] He_2016 [194] Gierlak_2017 [195] Zhao_2019b [196] Unique Jablon_2021 [197] Gu_2021 [198] Mohamad_2020 [2] Yan_2019 [199] Barbini_2018 [200] Khan_2016 [201] Biswas_2013 [202] Bai_2021a [203] Mohamad_2020 [2] Hizarci_2019 [204] Medina_2019 [205] Chen_2002 [206] Chen_2002…”

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

“…Comparison Tarek_2020 [186] Sakthivel_2014 [207] From the classification of the p2 papers of Table 3, iy emerged that frequency domain (spectral methods) and time-frequency domain methods are the more applied ones, while the techniques based only on time domain have a limited application. The largest number of works refers to time-frequency domain methods.…”

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

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A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In [66] an experimental system to acquire vibration and acoustic emission (AE) signals from faulted bearings methodology based on cepstrum pre-whitening (CPW) is built and tested for vibration signals, and applied for both types of signals, for machine condition monitoring. A comparative study between three advanced signal processing methods for the vibratory diagnosis of rotating machines working in industrial conditions is proposed in [67]. Cyclostationary analysis, empirical mode decomposition (EMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are used for detection of mechanical defects of a turbofan machine.…”

Section: Other Approaches Used In Vibration Analysismentioning

confidence: 99%

Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview

Popescu

Aiordăchioaie

Culea-Florescu

2021

Int J Adv Manuf Technol

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The paper presents some basic tools for vibration signals with application in predictive maintenance of rotating machines. After an overview of the maintenance approach, the condition monitoring in predictive maintenance is discussed. Also, signal processing in vibration monitoring, making use of some basic tools as change detection, independent component analysis, time-frequency analysis, and energy distribution in time-frequency plane are presented. These techniques can be combined in a general approach, offering new possibilities for more robust detection of changes in vibration signals and assuring proactive actions in predictive maintenance.

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“…As for the endpoint effect, there are many methods [17][18][19] to suppress it. CEEMDAN is more effective in aspects such as mechanical failure detection [20][21][22][23][24][25] and model optimization [26][27][28]; Patricio Fuentealba et al [29] used CEEMDAN to assess the condition of the fetus during delivery; Yao and Liu [30] applied MPE to the identification of EEG signals; Hu et al [31] combined CEEMDAN with MPE to identify the state of ball mills under different loads; Wang et al [32] proposed an optimized filtering method that combines CEEMDAN with MPE. However, the completeness of the CEEMDAN algorithm itself needs to be strengthened further.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

He¹,

Wang²,

Gao³

et al. 2021

EESRJ

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Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

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Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment

Cited by 19 publications

References 30 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

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