Nonlinear model for condition monitoring of non-stationary vibration signals in ship driveline application

Cardona-Morales, Oscar; Avendaño, L. D.; Castellanos-Domínguez, G.

doi:10.1016/j.ymssp.2013.08.029

Cited by 27 publications

(6 citation statements)

References 11 publications

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“…Hence the problem of chirp parameter estimation has been assimilated to a contour (straight lines) extraction problem in an image and dedicated image processing tools have been used in [34] for solving this particular estimation problem. Other works related to the use of TF signal analysis tools for vibration signals can be found in [35,36].…”

Section: Time-frequency Distribution (Tfd) Based Methodsmentioning

confidence: 99%

Vibration Signal Parameter Estimation in Variable Speed: Algorithms and performance bounds

Abbadi¹,

Capdessus²,

Abed-Meraim³

et al. 2021

ENPESJ

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Vibration signal parameter estimation for rotating machinery diagnostics operating under variable speedconditions is considered. At first, we provide a brief survey of existing methods for Quadratic Phase Signal (QPS)parameter estimation. Then, we introduce improved solutions for the general QPS case and the Order QPS (O-QPS)case, respectively. For all considered cases (namely the QPS, O-QPS with tachometer and O-QPS without tachometer),we develop the Cramer Rao Bounds to assess and compare the estimation performance limits for each model. Finally, wecompare the performance of all considered methods and highlight, in particular, the gain of the proposed solutions.

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Section: Time-frequency Distribution (Tfd) Based Methodsmentioning

confidence: 99%

Vibration Signal Parameter Estimation in Variable Speed: Algorithms and performance bounds

Abbadi¹,

Capdessus²,

Abed-Meraim³

et al. 2021

ENPESJ

View full text Add to dashboard Cite

show abstract

“…Huang_2021 [115] Attoui_2020 [116] Wang_2019b [28] Klausen_2019 [117] Dybała_2018 [118] Qiu_2018 [119] Li_2016d [120] Dolenc_2016 [121] Harmouche_2015 [122] Hafeez_2003 [123] Zakhezin_2010 [124] Wang_2019c [125] Feng_2017 [126] Guoji_2014 [127] Gelman_2005 [128] Elbhbah_2013 [129] Cardona-Morales_2014 [130] Time-frequency-based methods…”

Section: Fft Order Domain Envelope Spectrum and Envelope Order Spectrum Cepstrummentioning

confidence: 99%

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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“…TF-based techniques have been widely used and are deemed effective for speed extraction. Other methods include band-pass filtering and phase demodulation (Urbanek et al 2011(Urbanek et al , 2013, Teager Kaiser energy operator (Randall and Smith 2016), and Kalman filter (Cardona-Morales et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

et al. 2022

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Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.

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Nonlinear model for condition monitoring of non-stationary vibration signals in ship driveline application

Cited by 27 publications

References 11 publications

Vibration Signal Parameter Estimation in Variable Speed: Algorithms and performance bounds

Vibration Signal Parameter Estimation in Variable Speed: Algorithms and performance bounds

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

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