An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors

Chen, Zengshun; Yuan, Chenfeng; Wu, Haofan; Zhang, Likai; Li, Ke; Xue, Xuanyi; Wu, Lei

doi:10.3390/app12189027

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Its principle is to improve the distribution interval of signal extreme points by introducing zero-mean white noise to initial input signal, then compute the average of the modal components obtained from multiple EMD decompositions to eliminate the mixed white noise. Through this method, not only can the real modal component be obtained, but also the influence of external noise can be eliminated, so as to effectively suppress the mode mixing phenomenon generated by EMD, obtain more accurate envelope, and make the original characteristics of the signal better highlighted [23]. The decomposition process is as follows:…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Tang,

Lv,

Jia

et al. 2024

Meas. Sci. Technol.

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Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation（SCSSA）is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition (EEMD) to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine (SVM) for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution (MED), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization (MVO) and particle swarm optimization (PSO) algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.

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Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Tang,

Lv,

Jia

et al. 2024

Meas. Sci. Technol.

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“…=M. 24,25 The original data will be decomposed into eight IMF components by the EEMD script, and then analyzed with the original data in the correlation and energy ratio program. Correlation coefficient can directly reflect the degree of correlation between data.…”

Section: Axial Trajectory Purification Theorymentioning

confidence: 99%

Electro-mechanical braking system spindle unbalance axis trajectory purification and feedback method

Jin

Chen

Wang

et al. 2023

Advances in Mechanical Engineering

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The brake disc spindle of the electromechanical braking system will be accompanied by vibration unbalanced fault during the rotation operation, which will affect the braking performance of the brake system. In view of this phenomenon, based on the ensemble empirical mode decomposition algorithm and related energy operation theory, an offline purification program for the unbalanced axial trajectory of the spindle of electromechanical braking system are designed. Meanwhile, an online braking control feedback procedure for the axial trajectory are designed based on Cspace controller. Based on the designed program and braking theory, an experimental bench of the electro-mechanical braking system was set up, and experiments were conducted on the purification of the axial trajectory and the braking of the fault feedback respectively. The results show that the EEMD algorithm and the related energy operation theory can purify the unbalanced axial trajectory of the brake discs of the braking system and draw an ideal axial trajectory fault map; meanwhile, the experimental data of the fault feedback braking shows that the time required from the unbalanced fault monitoring to the completion of the braking process of the braking system is 1.278 s, which can effectively achieve the purpose of emergency braking in case of sudden failure. Through research, a new idea is provided for the development of electro-mechanical braking system fault detection and feedback.

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“…Traditional displacement and temperature sensors can only detect 1D deformation at a single point within a limited measurement range. [16][17] As contact-based measuring devices, highcoverage installation on the vast exterior wall of LNG storage tank poses significant challenges, if not being entirely unfeasible. As contact-based measuring devices, it is challenging to be mounted on the huge exterior wall with high coverage.…”

Section: Introductionmentioning

confidence: 99%

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

Gao,

Guo,

Wang

et al. 2024

Advcd Theory and Sims

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Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.

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An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors

Cited by 11 publications

References 31 publications

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Electro-mechanical braking system spindle unbalance axis trajectory purification and feedback method

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

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