A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

Sun, Hongchun; Fang, Liang; Zhao, Feng

doi:10.21595/jve.2018.20073

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Therefore, the separation of mixed signals can be completed by performing non-Gaussian maximization operation on mixed signals. The independent component analysis algorithm [24] computes the 𝐵 and 𝑆 matrices using kurtosis or negative entropy by maximizing the non-Gaussian property of the source signal. Fast independent component analysis finds the direction of maximizing non-Gaussian measurement through projection pursuit [25].…”

Section: Fast Independent Component Analysismentioning

confidence: 99%

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Li,

Zhang,

Wang

2024

J. vibroeng.

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Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

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Section: Fast Independent Component Analysismentioning

confidence: 99%

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Li,

Zhang,

Wang

2024

J. vibroeng.

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“…The features of MV, RMSV, and VARV indicate the amplitude and energy over the time domain of the signal, while those of SC, KC, and SF reflect the distribution situation over the time domain. 24 Table 1 summarizes the six statistical features, where N is the number of sampling points.…”

Section: ) Statistical Features Extracted By Time-domain Analysismentioning

confidence: 99%

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

Lv¹,

Xiong²,

Wang³

2023

The International Journal of Acoustics and Vibration

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Performance degradation assessment (PDA), as an important part of health management, is playing a crucial role in evaluating the degenerate state of mechanical equipment. To evaluate the performance degradation state more effectively by using monitoring data of mechanical equipment, a PDA method based on improved degradation feature extraction technology and considering sample completeness is proposed in this paper. Firstly, multiple degradation features, including statistical features and intrinsic energy features, are extracted to construct a high-dimensional feature set calculated by time-domain analysis and the EMD method. Then, a sensitive feature set, which has significant robustness, correlation, and monotonicity in the degenerate process, is reduced and processed by the improved PCA method to obtain the final health index. Secondly, the degradation assessment model is established by considering the completeness of the samples. As for the complete sample dataset, which has both normal and failure state data, a logistic regression model (LRM) is built to assess the performance degradation status. In addition, an SVDD model is established for the incomplete sample dataset, which only has normal state data. Finally, the reliability of the proposed method is verified by the public XJTU-SY dataset, and the comparative experiment further verifies the superiority of this method.

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“…Step 4: The second penalty factor ensures that the signal still offers better reconstruction accuracy under high noise conditions. The Lagrange multiplier maintains strict constraints, and the augmented Lagrange formula is [36][37][38]…”

Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

et al. 2020

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As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

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A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA

Cited by 10 publications

References 10 publications

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

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