Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network

Chavarette, Fábio Roberto; Souza, Simone Silva Frutuoso de; Chavarette, Fábio Roberto; Lopes, Mara Lúcia Martins; Turra, Antonio Eduardo; Júnior, Vicente Lopes

doi:10.4028/www.scientific.net/amr.1025-1026.1107

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…The results demonstrate the successful application of ANNs in predicting strains for various structural components of the aircraft, including the fuselage, wings, and tailplanes, and also discuss the technologies, strategies, and solutions employed for building and training the ANNs. Lima et al performed aircraft damage detection using ARTMAP-Fuzzy-Wavelet ANN to assist the inspection process for aircraft structures [106]. For damage assessment, the suggested technique integrates signal modeling and simulation using a numerical model.…”

Section: Machine Learning Approaches In Sphmmentioning

confidence: 99%

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Khalid,

Song,

Azad

et al. 2023

Mathematics

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This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

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Section: Machine Learning Approaches In Sphmmentioning

confidence: 99%

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Khalid,

Song,

Azad

et al. 2023

Mathematics

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“…Methods using mathematical models: in 2005, the generalized parity vector (GPV) extension model was proposed by Omana et al [4], and a system fault detection and isolation method based on this GPV model was demonstrated in 2007 [5]. An airplane structural fault detection method used a numerical model to perform simulation and modeling; Fernando et al [6] verified the effectiveness of this method.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection Method for Wi-Fi-Based Smart Home Devices

Cheng

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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At present, the dynamic nature and unstable network connections in the deployment environments of Wi-Fi-based smart home devices make them susceptible to component damage, crashes, network disconnections, etc. To solve these problems, researchers have used various fault detection methods, such as alarming when monitored fault parameters exceed the preset values, model-based mathematical methods, device signal processing-based methods, and artificial intelligence-based methods. However, these methods require large numbers of fault parameters, the model are complex, and their fault detection accuracy is relatively poor. To more quickly and accurately detect faults in smart home devices and ensure the continuity of people’s daily work and lives, this paper analyzes both the Wi-Fi traffic characteristics of smart home devices and the complexity and difficulty of traditional fault detection methods and proposes a fault detection method based on TDD (Throughput and Delay Distribution). This method obtains throughput and data packet delay distribution by capturing Wi-Fi communication and sending test data. By dividing the throughput into heartbeat data and command information, we can calculate the real-time throughput and further calculate the similarity between the real-time throughput and the throughput in database. Also, the resulting delay distribution is compared with the probability distribution of delay in the database. When the throughput values are sufficiently similar and the delays are all in the normal range, the smart home secure devices are functioning properly. The experimental results show that the proposed TDD method can detect faults in household devices in real time and that it achieves high recall and good detection accuracy in Wi-Fi communication environment.

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“…Reference [14] was shown a SHM based on ARTMAP-Fuzzy neural network and wavelet transform to diagnose faults in buildings. In [15] a hybrid method based on ARTMAP-Fuzzy neural network and wavelet transform to diagnose failures in aluminum beams was presented.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Intelligence Applied to Structural Failures Diagnosis

Souza¹,

Chavarette²,

Lima³

2022

Open Science Research III

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This chapter presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.

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Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network

Cited by 14 publications

References 11 publications

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Fault Detection Method for Wi-Fi-Based Smart Home Devices

A Hybrid Artificial Intelligence Applied to Structural Failures Diagnosis

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