Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

Zheng, Hairong; Liu, Junhao; Duan, Shanxu

doi:10.1155/2018/3496870

Cited by 8 publications

(15 citation statements)

References 13 publications

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“…The proposed HHT structure is shown in Figure 2 . First, the frequency shifting [ 49 ] is applied to reduce the mode mixing. Second, the BLSTM-based EMD is performed to compute the IMFs of inertial data and noise assistance analysis.…”

Section: Methods For Fault Diagnosis Of Mems Inertial Sensors In Unmentioning

confidence: 99%

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Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

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Section: Methods For Fault Diagnosis Of Mems Inertial Sensors In Unmentioning

confidence: 99%

“…The proposed method is used to detect ice formation on aircraft. In [ 49 ] Zheng et al presented a flutter test method using HHT, and the method mitigated the mode mixing effect in HHT. In [ 50 ], an improved EMD, called IEEMD, added Gaussian noise into the EMD operation.…”

Section: Related Workmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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“…Flutter signals are typically processed based on system stability [2], where flutter is considered an unstable phenomenon in the structural system as an appropriate dynamic data model is established for subcritical test signals. The dynamic data modeling places model parameters under certain criteria to define "stability" and calculate the stability parameters accordingly.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

Duan

Zheng

Liu

2019

International Journal of Aerospace Engineering

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Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. In this study, a signal processing method was established to use the structural response signals from an aeroelastic model to classify flutter signals via deep learning algorithm. This novel flutter signal processing and classification method works by combining a convolutional neural network (CNN) with time-frequency analysis. Flutter characteristics are revealed in both time and frequency domains, which are harmonic or divergent in the time series; the flutter model energy is singular and significantly increases in the frequency view, so the features of the time-frequency diagram can be extracted from the dataset-trained CNN model. As the foundation of the subsequent deep learning algorithm, the datasets are placed into a collection of time-frequency diagrams calculated by short-time Fourier transform (STFT) and labeled with two artificial states, flutter or no flutter, depending on the source of the signal measured from a wind tunnel test on the aeroelastic model. After preprocessing, a cross-validation schedule is implemented to update (and optimize) CNN parameters though the trained dataset. The trained models were compared against test datasets to validate their reliability and robustness. Our results indicate that the accuracy rate of test datasets reaches 90%. The trained models can effectively and automatically distinguish whether or not there is flutter in the measured signals.

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“…However, they are not suited for the signal of complex construction. A self-adaptive method for non-stationary signals and nonlinear is called Empirical mode decomposition (EMD) [19], and it has been successfully implemented for; (a) fault diagnosis [20], (b) wind energy [21], (c) flight flutter [22], (d) image processing [23], (e) health monitoring [24], (f) electroencephalogram (EEG) analysis [25], and (g) electrocardiogram (ECG) signals [26]. Moreover, it still flops to disintegrate a signal within the existence of a high trend.…”

Section: Introductionmentioning

confidence: 99%

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

Algburi

Gao

2019

Energies

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In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.

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Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

Cited by 8 publications

References 13 publications

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

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