Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model

Yang, Jingli; Chang, Yongqi; Gao, Tianyu; Wang, Jianfeng

doi:10.3390/app10062056

Cited by 20 publications

(8 citation statements)

References 38 publications

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“…This method can effectively extract the fault features of reciprocating pump vibration signal, and the test patterns can be accurately identified with the established classification model. Reference [22] proposed a new fault prediction method for rotating machinery based on CEEMDAN. The results demonstrate that this approach is superior to other methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Shang

et al. 2021

Entropy

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Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Shang

et al. 2021

Entropy

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“…Rotating machinery structure is the part with high damage rate in all kinds of mechanical structure faults [1]. Timely detection of rotating machinery faults can avoid property losses and personal safety accidents, so it is of great significance to analyze rotating machinery faults [2]. Rotating machinery is mainly composed of gearbox, rotor, shaft and rolling bearing.…”

Section: Introductionmentioning

confidence: 99%

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

Wang

Zhang

et al. 2021

IEEE Access

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Cepstrum coefficient and decision tree classification provide a feasible method for mechanical fault diagnosis. However, this method has the problems of low accuracy of feature extraction and low reliability of feature classification. The efficiency and classification results of this method are not ideal. Therefore, this paper proposes a feature classification method of frequency cepstrum coefficients based on weighted extreme gradient boosting. The proposed method is mainly divided into two parts: feature extraction and feature classification. In the part of feature extraction, Considering the limitation of Mel frequency scale in MFCC, an adaptive frequency cepstrum coefficient (AFCC) method is proposed, which can adaptively obtain the frequency scale of the corresponding signal, thus improving the feature accuracy extracted by MFCC method. In the aspect of feature classification, XGboost algorithm has strong learning ability, which easily leads to over fitting and affects the classification accuracy, A weighted extreme gradient boosting (WXGB) method is proposed, and an improved distance evaluation method (IDE) is introduced to modify the feature weights learned by XGboost, so as to improve the prediction accuracy of the classification model. In order to verify the effectiveness of the method, the vibration signals of rolling bearings with different fault types are taken as samples to carry out feature extraction and classification. The results of feature extraction and classification of different methods are compared, and the effectiveness of the proposed feature classification method based on weighted extremum gradient boosting frequency cepstrum coefficient in rolling bearing fault diagnosis is verified.

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“…Instead of approximating the nonlinear function, it uses a series of deterministic samples to approximate the posterior probability density of the state variables. Compared with extended Kalman filter (EKF), neither the derivative calculation of Jacobian matrix is required [24,25], nor the higher-order terms are ignored during linearization, and thus the calculation accuracy of nonlinear distribution statistics is higher than EKF [26][27][28]. In addition, Yang et al studied the health management of metal roof structure [29], Li et al conducted multi-factor analytic hierarchy process [30], and Sheng et al carried out multiobjective optimization for power system [31], all of these researches have the important reference significance for health management of the space station system.…”

Section: Introductionmentioning

confidence: 99%

UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station

et al. 2021

Applied Sciences

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Electrolytic oxygen generation system (EOGS) is the only system that can provide oxygen for astronauts in a physicochemical regenerative way in a long-term manned spacecraft. In order to ensure that the astronauts in the cabin can obtain a continuous and enough oxygen supply, it is necessary to carry out real-time condition monitoring and fault diagnosis of the EOGS. This paper deals with condition monitoring and fault diagnosis of the EOGS. Firstly, the dynamic model of the system is established based on the principle electrolysis for actual oxygen production system and the state observer of the system has been designed by using unscented Kalman filter (UKF). The total pressure in the cabin and the partial pressure of oxygen in the electrolytic cell can be observed. Then, considered the actual conditions of the manned space mission with one more astronaut, i.e., 3 astronauts, the simulation experiment is carried out. The simulation results show that the method can effectively estimate the system state, and it is of great significance to ensure the normal operation of the electrolytic EOGS system in the space station.

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Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model

Cited by 20 publications

References 38 publications

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station

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