Identification and separation of coupled vibration sources in multi-rotor gas turbines under time-varying speed conditions

Song, Chao; Cheng, Wei; Chen, Xuefeng; Wang, Jun; Qiao, Baijie; Ge, Xiangdong; Gao, Lin; Huang, Hai

doi:10.1016/j.ymssp.2022.110037

Cited by 13 publications

(6 citation statements)

References 45 publications

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“…After the transformation, the relevant frequency components could be revealed by spectrum analysis methods. Researchers at various universities, such as The University of New South Wales [46] , [47] , [48] , [49] , University of Lyon [50], [51], [52], Beijing Jiaotong University [53], [54], [55], [56], Xi'an Jiaotong University [57], [58], [59], [60] , Beijing University of Technology [45] , [61] , [62] , and University of Ottawa [63], [64], [65], have contributed a lot to this topic.…”

Section: Spectrum Analysis-based Diagnosis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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Rotating machinery often works under time-varying speeds, and nonstationary conditions as well as harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods including nonstationary signal processing methods and data-driven methods have been developed. This paper presents a comprehensive review on the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation and generalized demodulation, are reviewed. Second, the time-frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction-and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed.We not only review the relevant state-of-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages as well as the challenges that will be encountered when applying them to industrial applications. This paper is expected to provide new graduate students, institutions and companies with a preliminary understanding of the methods on this topic.

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Section: Spectrum Analysis-based Diagnosis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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“…A summary of these parameters are given in table 5. Accordingly, the parameter optimization ranges are set as: Level = [Level min , Level max ], TPTR = [1,4]…”

Section: Verification Of the Awd Methodmentioning

confidence: 99%

“…Vibration signal processing is an effective approach for rotating machinery fault diagnosis [4,5]. Fault features can be extracted from vibration signals and used to evaluate the health condition of the machines.…”

Section: Introductionmentioning

confidence: 99%

A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index

Zhou¹,

Zhang²,

Lu³

et al. 2023

Meas. Sci. Technol.

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Feature extraction from vibration signals plays a vital role in rotating machinery fault diagnosis. The noise contained in the signals will interfere with the fault feature extraction result. Wavelet denoising is a commonly used method to reduce the noise, but its parameters are generally selected based on subjective experience. With this problem in mind, an adaptive wavelet denoising method is proposed in this paper. Using permutation entropy to evaluate the signal noise level and taking its minimum value as the fitness function, the intelligent optimization algorithm is applied to optimize the wavelet denoising parameters. Based on this adaptive wavelet denoising method and a synthetic detection index, a new feature extraction approach is proposed. Results from simulation experiments and engineering applications prove that the signal denoising performance of the adaptive wavelet denoising method and the results of the fault feature extraction approach are satisfactory.

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“…[14][15][16][17][18][19] However, unlike the traditional forms of the POD technique that use spectral methods to isolate frequencies of interest, a new formulation is introduced here that fuses POD with a Vold-Kalman order tracking filter. [20][21][22][23][24] The motive for this is to improve the accuracy of the low-order reconstruction for instances where only a small number of discrete frequencies are of interest. The outline of the approach begins with a review of POD using a kernel comprised of auto-and cross-spectral densities of space-time waveforms, followed by a explanation of the Vold-Kalman order tracking filter and the means by which the two are joined.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…The denominator in this expression comprises the filter bandwidth Δf = nδf, and is chosen to be a small fraction of the sampling frequency Δf/f s ( 0.01. To minimize equation ( 22), the first derivative of the loss function J is taken with respected to the conjugate of the complex envelope, and set to zero, such that ∂J ∂s k ðtÞ * ¼ 0 (24) Combining equations ( 19), (20), and ( 24), an expression for the complex envelop unfolds…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

The space-time structure of sound produced by stacked rotors in hover using Vold-Kalman filters and proper orthogonal decomposition

Tinney,

Zhao-Dubuc,

Valdez

2023

International Journal of Aeroacoustics

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Proper orthogonal decomposition and the Vold-Kalman order tracking filter are combined to evaluate the most energetic components of the sound field produced by a coaxial, co-rotating rotor in hover. The study leverages the database generated by Tinney and Valdez (2020; AIAA J. vol. 58, no. 4) comprising an array of eight stationary microphones oriented to capture the acoustic near-field below the rotor disk plane where ground observers are expected to reside and where concerns over community annoyance are greatest. Changes to rotor conditions focus primarily on rotor speed and index angle (angular separation between the upper and lower rotors), as this has the greatest effect on the sound field produced by stacked rotors by way of the constructive and destructive interference of the sound generated, separately, by the upper and lower rotors. Proper orthogonal decomposition is performed using a kernel constructed from auto and cross-spectral densities of the spatially coherent sound field. The eigenvectors demonstrate the spatial extent of the sound field for discrete frequencies corresponding to the first few blade pass frequency harmonics. In order to improve the clarity and accuracy of the low-order reconstructions, a second generation, Vold-Kalman multi-order tracking filter is employed to isolate discrete frequencies. Relative to conventional spectral filtering methods, the Vold-Kalman filter is performed in the time-domain and is shown to accurately isolate discrete tones while preserving changes to the phase of the signal. Findings from this analysis reveal the effect of index angle on the spatial make-up of the coherent sound field produced by hovering stacked rotors.

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Identification and separation of coupled vibration sources in multi-rotor gas turbines under time-varying speed conditions

Cited by 13 publications

References 45 publications

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index

The space-time structure of sound produced by stacked rotors in hover using Vold-Kalman filters and proper orthogonal decomposition

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