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Automatic tracking of natural frequency in the time–frequency domain for blade tip timing EI
期刊论文 | 2022 , 516 | Journal of Sound and Vibration
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Abstract :

Natural frequency is an essential parameter for rotating blade condition monitoring. Various previous simulations and experiments have shown that multiple signal classification (MUSIC) has the advantage of filtering out the synchronous frequency component to make the natural frequency more prominent in the frequency domain. However, the negative effect of this characteristic is the poor performance in the resonance area because of the overlap between the synchronous frequency component and the natural frequency. This effect results in the disconnection of the natural frequency line in the resonance area. Thus, morphological filtering and mean absolute error (MAE)-based curve fitting are applied to robustly extract and restore the natural frequency line. Based on this method, automatic tracking of the natural frequency in the time–frequency domain is proposed in this study. Additionally, the mathematical principle of the inherent characteristic of MUSIC to filter out synchronous frequency components is first mentioned and explored herein. Furthermore, simulations and experiments under variable rotating frequencies are conducted to show that the proposed method can track the natural frequency under variable operating conditions. © 2021 Elsevier Ltd

Keyword :

Frequency domain analysis Timing circuits Natural frequencies Condition monitoring Curve fitting

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GB/T 7714 Wang, Zeng-Kun , Yang, Zhi-Bo , Li, Hao-Qi et al. Automatic tracking of natural frequency in the time–frequency domain for blade tip timing [J]. | Journal of Sound and Vibration , 2022 , 516 .
MLA Wang, Zeng-Kun et al. "Automatic tracking of natural frequency in the time–frequency domain for blade tip timing" . | Journal of Sound and Vibration 516 (2022) .
APA Wang, Zeng-Kun , Yang, Zhi-Bo , Li, Hao-Qi , Cao, Jia-Hui , Tian, Shao-Hua , Chen, Xue-Feng . Automatic tracking of natural frequency in the time–frequency domain for blade tip timing . | Journal of Sound and Vibration , 2022 , 516 .
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Impact force reconstruction and localization using nonconvex overlapping group sparsity EI
期刊论文 | 2022 , 162 | Mechanical Systems and Signal Processing
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Although extensively studied, impact force identification is still a challenging task. When the location of the impact force is unknown, an under-determined problem is usually required to be tackled. In this paper, a novel impact force identification method based on the nonconvex overlapping group sparsity(NOGS) is proposed, allowing to localize the impact and recover its time history simultaneously from quite limited measurements(i.e., the number of responses is less than the number of potential impact locations). The NOGS not only enriches the prior information by taking the group sparsity structure of impact forces into consideration, but enhances the sparsity and the accuracy of estimated amplitude via its nonconvexity. A new algorithm, named fast nonconvex overlapping group sparsity algorithm(FaNogSa), derived in the light of the Majorize-Minimization(MM) principle is utilized to minimize the nonconvex objective function. Simulations and experiments are both implemented systematically on a stiffened composite structure to validate the proposed method, and two strain gauges are utilized to monitor 54 potential impacts. The corresponding results, comparing to the plain nonconvex(atan) regularization and the standard 1-norm regularization, say that the proposed method is able to localize the impact and at the same time recover its time history accurately, while under the same measuring conditions the nonconvex(atan) method and the 1-norm method usually fail. © 2021 Elsevier Ltd

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GB/T 7714 Liu, Junjiang , Qiao, Baijie , Chen, Yuanchang et al. Impact force reconstruction and localization using nonconvex overlapping group sparsity [J]. | Mechanical Systems and Signal Processing , 2022 , 162 .
MLA Liu, Junjiang et al. "Impact force reconstruction and localization using nonconvex overlapping group sparsity" . | Mechanical Systems and Signal Processing 162 (2022) .
APA Liu, Junjiang , Qiao, Baijie , Chen, Yuanchang , Zhu, Yuda , He, Weifeng , Chen, Xuefeng . Impact force reconstruction and localization using nonconvex overlapping group sparsity . | Mechanical Systems and Signal Processing , 2022 , 162 .
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FRF-based lamb wave phased array EI
期刊论文 | 2022 , 166 | Mechanical Systems and Signal Processing
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Abstract :

In the former work, a SHM system was proposed by Kudela et al based on the concept of Lamb wave focusing by the piezoelectric array. According to the pre- and post-compensations, the Lamb wave is focused on the interested point. This allows the system to scan the structure point by point, and then get the damage localization results with high-resolution compared with the beamforming method. However, the efficiency of the method is limited by the ways of signal processing and scanning: 1) the amount of measurement should be equal to the number of inspection points; 2) and the compensation calculations must be done for every measurement. The situation is further compounded by the average step for inspection points. 3) the central frequency of excitation is fixed in the whole processing, to optimize the input waveform means redoing the scan. To address these problems, a novel mode of signal processing for the piezoelectric phased array is proposed based on the Frequency Response Function (FRF). In the present method, the point-by-point scan is replaced by a single measurement for FRF with the 'Single Input, Multiple Output' (SIMO) mode, which ensures the scan work can be established in N times excitations (N is the number of piezoelectric wafer, much smaller than the amount of inspection points). Furthermore, the present method is based on FRF, which means the excitation waveform can be virtually selected and changed after recording. It provides a more flexible path for the piezoelectric array concept in SHM. Experiments shows that the developed method is effective for one-dimensional and two-dimensional arrays. The work enriches the intension and implementation of piezoelectric phased array. © 2021 Elsevier Ltd

Keyword :

Frequency response Damage detection Signal processing Antenna phased arrays Ultrasonic waves Surface waves Piezoelectricity

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GB/T 7714 Yang, Zhi-Bo , Zhu, Ming-Feng , Lang, Yan-Feng et al. FRF-based lamb wave phased array [J]. | Mechanical Systems and Signal Processing , 2022 , 166 .
MLA Yang, Zhi-Bo et al. "FRF-based lamb wave phased array" . | Mechanical Systems and Signal Processing 166 (2022) .
APA Yang, Zhi-Bo , Zhu, Ming-Feng , Lang, Yan-Feng , Chen, Xue-Feng . FRF-based lamb wave phased array . | Mechanical Systems and Signal Processing , 2022 , 166 .
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Deep-learning-based open set fault diagnosis by extreme value theory EI
期刊论文 | 2022 , 18 (1) , 185-196 | IEEE Transactions on Industrial Informatics
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Existing data-driven fault diagnosis methods assume that the label sets of the training data and test data are consistent, which is usually not applicable for real applications since the fault modes that occur in the test phase are unpredictable. To address this problem, open set fault diagnosis (OSFD), where the test label set consists of a portion of the training label set and some unknown classes, is studied in this article. Considering the changeable operating conditions of machinery, OSFD tasks are further divided into shared-domain open set fault diagnosis (SOSFD) and cross-domain open set fault diagnosis (COSFD) in this article. For SOSFD, 1-D convolutional neural networks are trained for learning discriminative features and recognizing fault modes. For COSFD, due to the distribution discrepancy between the source and target domains, the deep model needs to learn domain-invariant features of shared classes and separate features of outlier classes. Thus, by utilizing the output of an additional domain classifier, a model named bilateral weighted adversarial networks is proposed to assign large weights to shared classes and small weights to outlier classes during the feature alignment. In the test phase, samples are classified according to the outputs of the deep model and unknown-class samples are rejected by the extreme value theory model. Experimental results on two bearing datasets demonstrate the effectiveness and superiority of the proposed method. © 2005-2012 IEEE.

Keyword :

Convolutional neural networks Failure analysis Deep learning Fault detection

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GB/T 7714 Yu, Xiaolei , Zhao, Zhibin , Zhang, Xingwu et al. Deep-learning-based open set fault diagnosis by extreme value theory [J]. | IEEE Transactions on Industrial Informatics , 2022 , 18 (1) : 185-196 .
MLA Yu, Xiaolei et al. "Deep-learning-based open set fault diagnosis by extreme value theory" . | IEEE Transactions on Industrial Informatics 18 . 1 (2022) : 185-196 .
APA Yu, Xiaolei , Zhao, Zhibin , Zhang, Xingwu , Zhang, Qiyang , Liu, Yilong , Sun, Chuang et al. Deep-learning-based open set fault diagnosis by extreme value theory . | IEEE Transactions on Industrial Informatics , 2022 , 18 (1) , 185-196 .
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The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study EI
期刊论文 | 2022 , 168 | Mechanical Systems and Signal Processing
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Deep learning (DL)-based methods have advanced the field of Prognostics and Health Management (PHM) in recent years, because of their powerful feature representation ability. The data in PHM are typically regular data represented in the Euclidean space. Nevertheless, there are an increasing number of applications that consider the relationships and interdependencies of data and represent the data in the form of graphs. Such kind of irregular data in non-Euclidean space pose a huge challenge to the existing DL-based methods, making some important operations (e.g., convolutions) easily applied to Euclidean space but difficult to model graph data in non-Euclidean space. Recently, graph neural networks (GNNs), as the emerging neural networks, have been utilized to model and analyze the graph data. However, there still lacks a guideline on leveraging GNNs for realizing intelligent fault diagnostics and prognostics. To fill this research gap, a practical guideline is proposed in this paper, and a novel intelligent fault diagnostics and prognostics framework based on GNN is established to illustrate how the proposed guideline works. In this framework, three types of graph construction methods are provided, and seven kinds of graph convolutional networks (GCNs) with four different graph pooling methods are investigated. To afford benchmark results for helping further study, a comprehensive evaluation of these models is performed on eight datasets, including six fault diagnosis datasets and two prognosis datasets. Finally, four issues related to the performance of GCNs are discussed and potential research directions are provided. The code library is available at: https://github.com/HazeDT/PHMGNNBenchmark. © 2021 Elsevier Ltd

Keyword :

Geometry Graph neural networks Convolutional neural networks Deep learning Convolution Systems engineering

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GB/T 7714 Li, Tianfu , Zhou, Zheng , Li, Sinan et al. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study [J]. | Mechanical Systems and Signal Processing , 2022 , 168 .
MLA Li, Tianfu et al. "The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study" . | Mechanical Systems and Signal Processing 168 (2022) .
APA Li, Tianfu , Zhou, Zheng , Li, Sinan , Sun, Chuang , Yan, Ruqiang , Chen, Xuefeng . The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study . | Mechanical Systems and Signal Processing , 2022 , 168 .
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An enhanced adaptive notch filtering method for online multi-frequency estimation from contaminated signals of a mechanical control system EI SCIE
期刊论文 | 2021 , 32 (10) | MEASUREMENT SCIENCE AND TECHNOLOGY
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Real-time frequency information is extremely important in many fields of mechanical engineering, such as fault diagnosis, noise and vibration control, underwater acoustic detection, vehicle communication, etc. However, sometimes frequencies cannot be directly detected, making it important to quickly and accurately estimate the frequencies from contaminated signals of a mechanical system. An adaptive notch filter (ANF) is one of the most popular methods for online frequency estimation due to its simple structure and low computational complexity. However, ANF is a biased estimation if the signal contains uncorrelated noise. An enhanced adaptive notch filtering (EANF) method, which is able to reduce the frequency estimation bias and improve the estimation speed from contaminated signals, is proposed in this paper. Firstly, the limitations of the traditional ANF method are theoretically and numerically analyzed. Then, the principles of the proposed EANF method are formulated, including key parameter optimization and uncorrelated noise compensation in the update process. Afterwards, a multiple extension of the proposed EANF method is constructed using the adaptive simultaneous structure. The results of the numerical simulation show that the proposed method is superior to traditional ones. Finally, a two-stage vibration isolation system is established for experimental validation. The experimental results also demonstrate the effectiveness of the proposed method.

Keyword :

uncorrelated noise compensation vibration isolation platform adaptive notch filtering multi-frequency estimation

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GB/T 7714 Liu, Jinxin , Zhang, Qian , Yin, Ziyu et al. An enhanced adaptive notch filtering method for online multi-frequency estimation from contaminated signals of a mechanical control system [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (10) .
MLA Liu, Jinxin et al. "An enhanced adaptive notch filtering method for online multi-frequency estimation from contaminated signals of a mechanical control system" . | MEASUREMENT SCIENCE AND TECHNOLOGY 32 . 10 (2021) .
APA Liu, Jinxin , Zhang, Qian , Yin, Ziyu , Sun, Chuang , Chen, Xuefeng , Song, Zhiping . An enhanced adaptive notch filtering method for online multi-frequency estimation from contaminated signals of a mechanical control system . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (10) .
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A wavelet immersed boundary method for two-variable coupled fluid-structure interactions EI SCIE
期刊论文 | 2021 , 405 | APPLIED MATHEMATICS AND COMPUTATION
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In this paper, a wavelet immersed boundary (IB) method is proposed to solve fluid-structure interaction (FSI) problems with two-variable coupling, in which it is an interac-tion between fluid force and boundary deformation. This wavelet IB method is developed by introducing a wavelet finite element method to calculate the FSI force affected by the two-variable coupling. Furthermore, a boundary influence matrix and a series of B-spline wavelet delta functions are constructed to restrain the non-physical force oscillations. Fi-nally, several FSI problems are simulated, which include flows past a fixed circular cylinder and a crosswise oscillating circular cylinder, as well as an in-line oscillating circular cylin-der in a rest fluid. The numerical examples show that the new method is a simple and efficient method for two-variable coupled FSI problems. (c) 2021 Elsevier Inc. All rights reserved.

Keyword :

B-spline wavelet delta function Wavelet immersed boundary method Fluid-structure interaction Boundary influence matrix

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GB/T 7714 He, Yanfei , Zhang, Xingwu , Zhang, Tao et al. A wavelet immersed boundary method for two-variable coupled fluid-structure interactions [J]. | APPLIED MATHEMATICS AND COMPUTATION , 2021 , 405 .
MLA He, Yanfei et al. "A wavelet immersed boundary method for two-variable coupled fluid-structure interactions" . | APPLIED MATHEMATICS AND COMPUTATION 405 (2021) .
APA He, Yanfei , Zhang, Xingwu , Zhang, Tao , Wang, Chenxi , Geng, Jia , Chen, Xuefeng . A wavelet immersed boundary method for two-variable coupled fluid-structure interactions . | APPLIED MATHEMATICS AND COMPUTATION , 2021 , 405 .
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Sparse reconstruction for blade tip timing signal using generalized minimax-concave penalty EI SCIE
期刊论文 | 2021 , 161 | Mechanical Systems and Signal Processing
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Rotor blade health monitoring based on the non-contact blade tip timing (BTT) technique has already been proved to be an alternative method to the classical contact strain measurement method. However, the signal sampled by the BTT system is usually undersampled due to the limited BTT sensors. Sparse regularization in the framework of 1-norm has been introduced to identify the blade vibration parameter from the undersampled BTT data. However, the standard sparse regularization based on1-norm penalty generally generates an underestimated solution. Compared with 1-norm penalty, generalized minimax-concave (GMC) penalty as a non-convex penalty has the promising property of amplitude improvement. In this paper, a non-convex optimization model based on GMC penalty is developed for reconstructing the undersampled BTT signal to obtain the accurate blade-tip displacement and blade natural frequency. The optimization model based on GMC penalty is presented to find the global optimal solution for the sparse representation of the BTT signal even if GMC penalty turns out to be a non-convex regularizer. Additionally, the strategy of regularization parameter selection is provided through the blade tip timing simulator. The relationship between the noise level and the regularization parameter is established to provide the strategy of regularization parameter selection in experiment. Finally, the blade spin testing is carried out for measuring the blade vibration by BTT and strain gauge systems. Amplitudes and frequencies of reconstructed BTT signals are compared with the measurements of the strain gauge, which are transferred from the strain at the blade root to the displacement at the blade tip by using the conversion coefficient obtained from the finite element model. Both simulation and experiment demonstrate that compared with the 1-norm penalty, GMC penalty can reconstruct the blade-tip displacement and blade natural frequency with high accuracy. © 2021 Elsevier Ltd

Keyword :

Natural frequencies Convex optimization Strain gages Parameterization Timing circuits Strain measurement Signal reconstruction

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GB/T 7714 Xu, Jinghui , Qiao, Baijie , Liu, Junjiang et al. Sparse reconstruction for blade tip timing signal using generalized minimax-concave penalty [J]. | Mechanical Systems and Signal Processing , 2021 , 161 .
MLA Xu, Jinghui et al. "Sparse reconstruction for blade tip timing signal using generalized minimax-concave penalty" . | Mechanical Systems and Signal Processing 161 (2021) .
APA Xu, Jinghui , Qiao, Baijie , Liu, Junjiang , Ao, Chunyan , Teng, Guangrong , Chen, Xuefeng . Sparse reconstruction for blade tip timing signal using generalized minimax-concave penalty . | Mechanical Systems and Signal Processing , 2021 , 161 .
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Crack propagation monitoring of rotor blades using synchroextracting transform EI SCIE
期刊论文 | 2021 , 509 | Journal of Sound and Vibration
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Blade fatigue fracture is a serious aero-engine fault that may lead to an aircraft crash. It is essential for ensuring the safety of the aero-engine operation to monitor the blade crack propagation. Generally, the crack propagation of rotor blades can be detected by the natural frequency shift. The time-frequency analysis method with high time-frequency resolution can detect the natural frequency shift during crack propagation. In this paper, the synchroextracting transform with the advantage of high time-frequency resolution is proposed to extract the time-frequency feature of the vibration signal of the cracked blade. In simulation, the crack length is defined to increase exponentially. The blade finite element model is used to generate the simulated signals. Compared with short-time Fourier transform, synchroextracting transform with the advantage of high time-frequency resolution can detect the natural frequency shift of the cracked blade earlier. Furthermore, Chebyshev window is used to solve the problem of spectral leakage caused by an interval sampling strategy. In a spin testing, two blades without artificial cracks suddenly broke. Strain signals are used to verify the effect of time-frequency resolution on crack propagation monitoring. Short-time Fourier transform is difficult to monitor the crack propagation in the spin testing because of the interval sampling strategy. However, synchroextracting transform with the advantage of high time-frequency resolution can detect the crack through the natural frequency shift of the cracked blade. Finally, in order to reveal the damage of the cracked blade, the crack length is estimated qualitatively by combining the natural frequency and the rotational speed measured in experiment with the results of finite element analysis. The feasibility of the crack estimation method is verified by the experiment in a non-rotating case. © 2021 Elsevier Ltd

Keyword :

Finite element method Natural frequencies Frequency estimation Turbomachine blades Crack propagation Cracks Aircraft engines Engines

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GB/T 7714 Xu, Jinghui , Qiao, Baijie , Liu, Meiru et al. Crack propagation monitoring of rotor blades using synchroextracting transform [J]. | Journal of Sound and Vibration , 2021 , 509 .
MLA Xu, Jinghui et al. "Crack propagation monitoring of rotor blades using synchroextracting transform" . | Journal of Sound and Vibration 509 (2021) .
APA Xu, Jinghui , Qiao, Baijie , Liu, Meiru , Yang, Zhibo , Chen, Xuefeng . Crack propagation monitoring of rotor blades using synchroextracting transform . | Journal of Sound and Vibration , 2021 , 509 .
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Parameter identification of blade tip timing signal using compressed sensing EI
期刊论文 | 2021 , 42 (5) | Acta Aeronautica et Astronautica Sinica
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Despite the wide application of the blade tip timing as a non-contact measurement technique, its acquired displacement signal is undersampled in many situations. Compressed sensing is an effective means to reconstruct the undersampled signal. However, the reconstruction process introduces the regularization penalty to achieve sparsity, reducing the amplitude accuracy of the reconstructed signal at the same time, and affecting the accurate identification of blade vibration amplitude, which is highly significant in reconstructing the dynamic stress of the blade. This paper combines the design matrix of the blade vibration equation and the compressed sensing dictionary to identify parameters of the blade vibration equation without prior information. First, a compressed sensing dictionary is constructed according to the form of design matrix elements and the maximum vibration frequency of interests. Second, atoms are extracted from the dictionary according to the indices of non-zero elements in the sparse representation of the blade tip timing signal, and parameters of the vibration equation are calculated. Third, Blade Tip Timing (BTT) simulation results demonstrate that the proposed method can accurately identify parameters of the vibration equation with both single-mode and multi-mode vibrations. Finally, experimental results of rotor blades show that the relative error of the identified natural frequency between using strain gage and using blade tip timing with compressed sensing is only 0.14%; the transmissibility of blade tip displacement to certain points strain of the four blades is compared and the maximum percentage of deviation from the mean is only 2.15%. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.

Keyword :

Strain gages Compressed sensing Timing circuits Signal reconstruction

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GB/T 7714 Xu, Jinghui , Qiao, Baijie , Teng, Guangrong et al. Parameter identification of blade tip timing signal using compressed sensing [J]. | Acta Aeronautica et Astronautica Sinica , 2021 , 42 (5) .
MLA Xu, Jinghui et al. "Parameter identification of blade tip timing signal using compressed sensing" . | Acta Aeronautica et Astronautica Sinica 42 . 5 (2021) .
APA Xu, Jinghui , Qiao, Baijie , Teng, Guangrong , Yang, Zhibo , Chen, Xuefeng . Parameter identification of blade tip timing signal using compressed sensing . | Acta Aeronautica et Astronautica Sinica , 2021 , 42 (5) .
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