Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Ebrahimkhanlou, Arvin; Salamone, Salvatore

doi:10.3390/aerospace5020050

Cited by 124 publications

(63 citation statements)

References 36 publications

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“…The paper tape denotes a substantial modification of the aerodynamics of the blade and this modification characterizes the noise produced by the blade in its rotation. In recent years, algorithms based on machine learning have been used to detect faults in machine functioning and control [50][51][52][53][54][55][56][57][58]. First, acoustic measurements were performed in an anechoic chamber and then these were analyzed to characterize the phenomenon [59][60][61][62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade.

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Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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“…The predicted RUS of an AE event would yield more insight to the overall degradation process of the SiC f -SiC m material since it would indicate the intensity of the deterioration. Even though previous studies did use those aforementioned supervised learning methods on AEs, their application to predict the RUS of SiC f -SiC m material is yet to be adapted [19,20], which is done in this work.…”

Section: Datasetmentioning

confidence: 99%

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

et al. 2020

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Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

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“…In a similar way, deep learning has also brought some wave of excitement to diagnostic SHM, although there are less works exploiting deep learning for diagnostic SHM in comparison to NDT. Apart from the work of Ebrahimkhanlou and Salomone [31], who used deep autoencoder (deep AE) for acoustic emission (AE) source localization and the work of Choy [32] and Oliveira et al [33] who used CNN for processing electromechanical impedance (EMI), we are not aware of any further works involving deep learning in diagnostic SHM at the time being (January 2019).…”

Section: State Of the Art: Advances Of Machine Learning And Its Applimentioning

confidence: 99%

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

Ewald

Groves

Benedictus

2019

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019

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A deep learning approach for structural health monitoring based on guided Lamb wave technique Ewald, Vincentius; Groves, Roger M.; Benedictus, Rinze ABSTRACTIn our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals and formalizes a generic method for end-to-end deep learning for SHM. The study case is limited to ultrasonic guided waves SHM. The sensor signal response from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is then fed into the CNN to be trained to obtain the neural weights. In this paper, we present the results of our investigation on CNN complexities that is needed to model the sensor signals based on simulation and experimental testing within the framework of DeepSHM concept.

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Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Cited by 124 publications

References 36 publications

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

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