Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

Bagherzadeh, Seyed Amin; Asadi, Davood

doi:10.1016/j.ymssp.2016.11.013

Cited by 18 publications

(18 citation statements)

References 36 publications

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“…We compared the proposed two-layer BLSTM-based EMD algorithm, which is trained by the RI(

) dataset, to the other EMD methods, with respect to the EMD performance on our MEMS inertial datasets. The compared methods were as follows: traditional EMD [ 43 ], multiobjective optimization-based EMD [ 48 ], frequency-shifting-based EMD [ 49 ] and noise assistance analysis-based EMD [ 50 ]. We first evaluated EMD performance that includes the IMFs and Hilbert spectrum of the proposed method and the orthogonality of the IMFs.…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the IMF decomposing performance, we compared the orthogonality index criterion presented by Bagherzadeh et al [ 48 ] as follows:

where

indicates the residual for computing

.…”

Section: Methodsmentioning

confidence: 99%

“…As in our proposed method EMD directly operates with BLSTM, our algorithm does not need various inner loop iterations. The largest time consumption of an EMD method in [ 48 ] is due to the time required for the genetic algorithm to perform the evolution computing. The similarity in time consumption among the EMD methods of [ 49 , 50 ] and traditional EMD is due to these three methods having the same sifting manner in EMD operation.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers are using HHT to perform signal analysis. In [ 48 ], Bagherzadeh et al enhanced the EMD algorithm using multi-object optimization, which introduced a genetic algorithm to determine the best decision parameters. The proposed method is used to detect ice formation on aircraft.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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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“…We compared the proposed two-layer BLSTM-based EMD algorithm, which is trained by the RI(

Section: Methodsmentioning

confidence: 99%

“…To evaluate the IMF decomposing performance, we compared the orthogonality index criterion presented by Bagherzadeh et al [ 48 ] as follows:

where

indicates the residual for computing

.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…A number of ice detection methods have been proposed for various structures and purposes . They have been proven to be effective and promising; however, according to their sensing and detection mechanisms, they provide different information about the ice.…”

Section: Introductionmentioning

confidence: 99%

Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors

Zhang

Zhou

2020

Struct Control Health Monit

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Summary Icing on the surface of wind turbine blades is one of the most critical problems for wind turbines installed at sites in cold climate. It can significantly affect the performance of the entire wind turbine. A number of deicing methods and techniques have been developed to address this problem. However, first of all, timely icing detection and estimation can provide the necessary information, such as the amount and location of icing, which are very useful for optimizing the deicing process. In this work, first, the thermodynamic processes during icing on the surface of wind turbine blades were presented to understand the characteristics of the varying temperature related to icing process. In the experimental section, a wind turbine blade model was tested in the low‐temperature laboratory. High‐performance distributed fiber optic sensors were attached to the surface of the wind turbine blade to measure the temperature change. This sensor can provide the temperature information as much as possible, which enables the identification of the initiation time of icing and the estimation of the amount of icing. As a result, the relationship between the amount of icing and the temperature change can be obtained. It indicates that the proposed estimation method is efficient and sensitive for ice monitoring of wind turbine blades. Finally, finite element analysis was implemented to explore the effects of airflow on the temperature curves.

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Automatic Landing Control of a Multi-Rotor UAV Using a Monocular Camera

Nabavi

Asadi

Ahmadi

2022

J Intell Robot Syst

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Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

Cited by 18 publications

References 36 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

Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors

Automatic Landing Control of a Multi-Rotor UAV Using a Monocular Camera

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