Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Jun, Tian; Yi, Guo-Wei; Fei, Chengwei; Zhou, Jie; Ai, Yanting; Zhang, Fengling

doi:10.1002/stc.2839

Cited by 10 publications

(4 citation statements)

References 43 publications

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“…Obviously, VMD-CSA can also extract the fault information of crane. Furthermore, the FFR value, the PFE, and the running time of CPU are calculated to quantitatively analyze the fault information extraction capability of different methods (i.e., APOVMD, 27 KEMI-VMD, 28 WOA-VMD, 29 FTVMD, 30 POVMD, 31 BAS-VMD, 32 ASME-VMD, 33 CFDSVMD, 34 VMD-CSA, 44 FK, 36 maximum Gini index deconvolution (MGID), 45 Gini index of the square envelope (GISE), 45 and the proposed method), and the results are displayed in Table 3. It is very obviously in Table 3 that the FFR value of the proposed method is higher than that of other 12 methods, which indicates that the proposed method has better crane fault detection capability.…”

Section: Simulation Analysismentioning

confidence: 99%

“…Geng et al 35 proposed the fast kurtogram (FK) to detect the resonance band of transient impulses based on the spectral kurtosis (SK), FK and its variants have been widely used in fault diagnosis. 36,37 As is well-known, kurtosis can detect the strength of the transient impulses, but it does not consider the cycle characteristic of the transient impulses, and it is difficult for kurtosis to distinguish impulsive noise and cyclic transient impulses in AE signal, which may lead to incorrect resonance band identification, especially when the signal noise ratio (SNR) is low. The periodicity can well distinguish impulsive noise and cyclic transient impulses, and the squared envelope spectrum (SES) infogram based on negative entropy, 38 diagnosis method based on multi-index fuzzy fusion, 39 and the envelope harmonic noise ratio (EHNR) based on autocorrelation 40 have been proposed successively.…”

Section: Introductionmentioning

confidence: 99%

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A progressive decomposing and double screening strategy of VMD for weak fault extraction of hoisting machinery

Zou

Lee

et al. 2022

Structural Health Monitoring

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To alleviate the difficulty of extracting weak fault features of hoisting machinery, a progressive decomposing and double screening strategy of variational mode decomposition (VMD) is presented in this paper. Firstly, the feasibility and effectiveness of extracting fault modes using progressive decomposition strategy is validated through numerical simulation, and it solves the problem of determining the mode number [Formula: see text] in traditional VMD. Secondly, a new index named energy fluctuation factor (EFF) is proposed. Specifically, EFF is more effective in detecting the signal periodicity compared with the kurtosis and the Shannon entropy (SE), and it is used to optimize the balance parameter [Formula: see text] of VMD. Thirdly, the criterion of double screening based on the kurtosis and the EFF is given to accurately localize and reconstruct the fault modes, and then Hilbert transform is utilized to demodulate the reconstructed mode. Finally, the numerical simulation and experimental and practical engineering applications verify that the proposed method can accurately extract the modes of weak fault and well solve the problem of determining the key parameters (i.e., [Formula: see text] and [Formula: see text] ) of VMD. Furthermore, the superiority of the proposed method is validated by comparing with other fault diagnosis methods.

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Section: Simulation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A progressive decomposing and double screening strategy of VMD for weak fault extraction of hoisting machinery

Zou

Lee

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Singh and Harsha achieved fault diagnosis of bearings at different speeds and loads through statistical parameter features after signal preprocessing [5]. Literature [6][7][8] have successfully applied different types of entropy to fault diagnosis. Frequency domain bearing fault diagnosis is mainly achieved through frequency characteristic parameters and spectrum analysis.…”

Section: Introductionmentioning

confidence: 99%

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

Yang,

Bai,

Tan

et al. 2024

Meas. Sci. Technol.

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For the issue of significant noise in the collected bearing fault signals, a new bearing fault diagnosis model based on mutual mapping of signals and images (MMSI) and sparse representation (SR) denoising is proposed. Firstly, the fault signal is divided into several segments with the same number of sampling points, and then arrange these segments in ascending order of rows. Secondly, convert the arranged signals into grayscale image and use dictionary learning for block denoising. Then, the de-noised grayscale image is restored to a signal in line order. Finally, k-nearest neighbor (KNN) is used for fault classification. To verify the performance of the proposed model, experiments are tested on 12 single working conditions and 30 multi working conditions on the Case Western Reserve University (CWRU) dataset and the Paderborn dataset. The experimental results indicate that compared with some existing models, the MMSI-SR-KNN model can not only accurately diagnose bearing faults in artificial damage experiments, but also performs better in real damage faults. This indicates that the model has good generalization ability between different datasets and working conditions.

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“…Each time, different data 709 is considered as the test set and repeated 30 times(M=30), 710 and the average accuracy is finally taken as the final model 711 accuracy. [11], [15], [16], [17], 729 [19], [21], [23], [24], representing peak-peak value, variance,…”

mentioning

confidence: 99%

Fault Diagnosis of Aeroengine Control System Sensor Based on Optimized and Fused Multidomain Feature

Gou

Ying-Xue

et al. 2022

IEEE Access

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Improving the efficacy and dependability of aeroengines requires timely and effective sensor fault diagnosis. Deep learning-based fault diagnosis method is a current research hotspot. To overcome some of the method's existing shortcomings and improve the reliability of fault diagnosis, this paper proposes a novel intelligent fault diagnosis framework with higher quality features and more effective fault classifiers. The proposed plan includes three stages. Firstly, multidomain features (time and frequency domain features) are extracted to describe the sensor's health from several dimensions. Secondly, the advanced Henry gas solubility optimization algorithm (HGSO) is applied to improve classification accuracy through feature selection, and the operating conditions and the features extracted by the network are fused as fault indicators. Finally, an adaptive deep belief network (ADBN) with relu-softsign combination activation layers, variable learning rate, and optimized network structure is proposed as the fault identifier. The advantages of the first two stages lie in the complete utilization of information and reducing the data dimension. In addition, the detection performance and the convergence speed is enhanced by the proposed ADBN. The experimental data are derived from a combination of measured and simulated data generated from the aeroengine model. The experimental results indicate that the improved method can produce better performance and outcomes than the unimproved methods for all fault scenarios, with a higher diagnostic accuracy of 98.1% and a reduced time of 98 s. The efforts of this study provide a efficient and adaptable way to aeroengine sensor fault diagnosis.INDEX TERMS Aeroengine control system, Henry's gas solubility optimization algorithm, adaptive deep belief network, sensor fault diagnosis, multidomain features. I. INTRODUCTION 20Aeroengine sensors must be reliable for aircraft performance 21 and flight safety [1], [2]. Aeroengines control and health 22 management are strongly dependent on accurate sensor mea-23 surements. However, the aeroengine sensors are prone to 24 faults because of the extreme working conditions, making the 25 engine run abnormally and even catastrophic events [3], [4]. 26 To prevent accidental faults and reduce maintenance costs, 27 an effective strategy is needed to ensure the reliability and 28 safety of the aeroengine. 29 application despite its excellent performance. Specifically, 51 an aeroengine is a complicated device with multi-field cou-52 pling, resulting in complex and difficult modeling. With 53 growing modeling uncertainty and nonlinear complexity, 54 diagnostic performance may decline. 55 In contrast, data-driven approaches do not need prior 56 knowledge, such as esoteric engine working principles and 57 complex modeling techniques [14]. It obtains fault infor-58 mation directly from the massive aeroengine running data. 59 Data-driven methods have received much attention from 60 researchers because of their advantages in describing non-61 linear functi...

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Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Cited by 10 publications

References 43 publications

A progressive decomposing and double screening strategy of VMD for weak fault extraction of hoisting machinery

A progressive decomposing and double screening strategy of VMD for weak fault extraction of hoisting machinery

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

Fault Diagnosis of Aeroengine Control System Sensor Based on Optimized and Fused Multidomain Feature

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