A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures (original) (raw)
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Advanced Materials Research, 2014
This paper presents a methodology to perform the monitoring and identification of flaws in aircraft structures using an ARTMAP-Fuzzy-Wavelet artificial neural network. This technique is used in the detection and characterization of structural failure. The main application of this method is to assist in the inspection of aircraft structures in order to identify and characterize failures as well as decision-making, in order to avoid accidents or air crashes. In order to evaluate this method, the modeling and simulation of signals from a numerical model of an aluminum beam was performed. The results obtained by the method are satisfactory compared to literature.
International journal of pure and applied mathematics, 2018
This paper presents a new hybrid methodology to diagnose failures in aeronautical structures using as a tool the Perceptron multi-layer artificial neural networks and ARTMAP-Fuzzy and the wavelet transform. The main application of this hybrid methodology. 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. The results demonstrate the robustness and accuracy of the methodology.
Brazilian Journal of Development, 2021
This paper 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.
This paper presents an ARTMAP-Fuzzy artificial neural network for monitoring and fault diagnosis in mechanical structures. The goal is to use the ARTMAP-Fuzzy neural network in the identification and characterization of structural failure. This methodology can help professionals in the inspection of mechanical structures, identifying and characterizing faults, in order to perform preventative maintenance and decision-making. In order to validate the methodology we propose the modeling and simulation of signals from a numerical model using an aluminum beam. The results show the efficiency, robustness and accuracy of the methodology adopted. Rate Training ( [0 1]): Controls the speed of adaptation of the network; Parameter monitoring ( a , b e ab [0 1]): Controls the resonant network, namely the parameter responsible for the number of categories created. Each ART module is consists of three layers: the input layer being (F 0 ), F 1 the compared layer, and F 2 the recognition layer, which performs the storage into categories. Data (I) supplied to the layer F 0 are in the form of complementary coding, i.e. Ia = [a a c ] and I b = [b b c ]. Layers F 1 and F 2 are connected by weights ( a J w in ARTa and b K w in ART b ). These weights are used in the process of choosing the category for each input vector (I) in F 1 the index J in F 2 is can define the choice function T j acoording (1), in which (^) is fuzzy operator "and". The selection of the category is performed as (2), J is active index of F 2 [1]. J J j a J a J a J
Applied Mechanics and Materials, 2014
This article presents the application and comparison of two techniques for intelligent computing to perform the analysis of the structural integrity of an aircraft structure. In this context, a ARTMAP-Fuzzy neural network and immunological negative selection algorithm are used in the identification and characterization of structural failure. The main application of these methodologies is to assist in the inspection of aircraft structures aiming at detecting and characterize flaws and decision making. To evaluate the methodology was performed modeling and simulation of signals from a numerical model using an aluminum beam. We performed a comparative analysis of methodologies, proving the efficiency of intelligent methods in the analysis of structural integrity. The results obtained by the method show efficiency, robustness and accuracy. To Evaluate the methodology was Performed modeling and simulation of signals from the numerical model using an aluminum beam. We performed a comparative analysis of methodologies, proving the efficiency of intelligent methods in the analysis of structural integrity. The results Obtained by the methods show efficiency, robustness and accuracy.
Fault Diagnosis on Steel Structures Using Arti cial Neural Networks
2009
The goal of this effort is to diagnose fault on steel structures by using non destructive techniques. Ultrasonic techniques are usually applied in engineering for faults determination, thickness measuring, adhesive layers, and in metallurgy to establish the quality of welds in metallic pieces. But the ultrasonic techniques could be difficult or impossible to apply in structures with reduced space, i.e. car frameworks. Acoustic signals have been employed since ancient times for detecting faults. Striking an object produces a sound whose differences may be heard when the object is damaged, therefore the vibration signals can be applied to detect differences into a metallic structure. Moreover, the Frequency Response Function (FRF) is used in this work to detect damages in metallic structures. The FRFs are used as input in an artificial intelligent system such as neural nets to detect damage. In general, non destructive evaluation is applied to detect and localize structure faults by u...
Application of artificial neural networks in the damage identification of structural elements
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Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification
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Neural Network Diagnostics of Aircraft Parts Based on the Results of Operational Processes
Radio Electronics, Computer Science, Control
Context. The problem of synthesis of an optimal neural network model for diagnostics of aircraft parts after operational processes is considered. The object of the study is the process of synthesis of neural network diagnostic models for aircraft parts based on the results of operational processes Objective is to synthesize neural network diagnostic models of aircraft parts after operational processes with a high level of accuracy. Method. It is proposed to research the use of two approaches to the synthesis of neural network diagnostic models. So, using a system of indicators, the topology of the neural network is calculated, which will be trained using the method of Backpropagation method in the future. The second approach is based on the use of a neuroevolutionary approach, which allows for a complete synthesis of the neural network, dynamically modifying the topology of the solution in addition to the parameters. the final decisions are compared in the accuracy of work on the tr...
Construction of an Artificial Neural Network-Based Method to Detect Structural Damage
Fracture Mechanics Applications [Working Title], 2019
This chapter shows the framework used to obtain data with which the artificial neural network (ANN) was developed. It describes its geometry, properties of the material, sections of structural elements, and loads used. Then, the numerical model of the framework under study is developed in structural analysis using SAP2000 ® software in order to obtain its modal parameters. In addition, a program made in MATLAB ® is shown, from which data with and without damage to the framework under study were obtained, and with which the ANN was developed. Data from the numerical model were used to corroborate data obtained with MATLAB ®. The neural model used in this work to detect structural damage is described. Data on damage were obtained simulating a plastic hinge in various elements of a test framework, varying the position of the hinge. The above resulted in obtaining various damage conditions for the same framework, which data thus obtained were used to develop the network. Damage conditions were hierarchized based on their fundamental periods in order to know where is more damage, depending on location of the hinge within the framework. Upon completion of the research, we have concluded that the methodology implemented to detect structural damage is rather simple. It was carried out in four steps.