scholarly journals Wavlet Decomposition based Diagnostic for Structural Health Monitoring on Metallic Aircrafts: Case of Crack Triangulation and Corrosion Detection

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Hamza Boukabache ◽  
Christophe Escriba ◽  
Sabeha Zedek ◽  
Jean-Yves Fourniols
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Control Method ◽  
Temporal Frequency ◽  
Time Frequency ◽  
Structural Health ◽  
Acoustic Signatures ◽  
Integrated Modular Avionics ◽  
Pulse Echo

This work focus on the structural health monitoring of aircrafts parts specimen structures made of 2024 Aluminum alloys using a reliable Joint Time Frequency Analysis calculation (Joint Temporal Frequency Analysis). In this paper we demonstrate the feasibility of a new non destructive control method capable to probe very large structures within a short time. The method we developed is based through a wide piezoelectric sensors network on a smart comparison between two acoustic signatures: the healthy structure response captured before the commissioning of the plane and “an after flight” response. The sensors network exploits the capability of piezoelectric patches to generate/measure specific Lamb wave’s modes. The system is therefore dynamically configured to localize mechanicals flaws using a triangulation algorithm that operates using different techniques like pitch-catch and pulse-echo. The aim of this paper is to highlight a methodology that is currently being integrated into reconfigurable qualified and certified hardware architecture. The idea behind is to interface the airplane's structure to an integrated modular avionics calculator (IMA).An analytic study is performed and tests to prove the proposed method feasibility on corroded and damaged structures specimens are provided at the end of this paper.

A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring

2020 ◽  
Vol 19 (6) ◽  
pp. 1963-1975 ◽  
Author(s):  
Yuequan Bao ◽  
Yibing Guo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Structural Health ◽  
Reconstructed Signal

Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time-varying parameters. Thus, it plays a significant role in structural health monitoring, such as data analysis, and nonlinear damage detection. Adaptive sparse time–frequency analysis is a recently developed method used to estimate an instantaneous frequency, which can achieve high-resolution adaptivity by looking for the sparsest time–frequency representation of the signal within the largest possible time–frequency dictionary. However, in adaptive sparse time–frequency analysis, non-convex least-square optimization is the most important and difficult part of the algorithm; therefore, in this research the powerful optimization capabilities of machine learning were employed to solve the non-convex least-square optimization and achieve the accurate estimation of the instantaneous frequency. First, the adaptive sparse time–frequency analysis was formalized into a machine-learning task. Then, a four-layer neural network was designed, the first layer of which was used for training the coefficients of the envelope of each basic functions in a linear space. The next two merge layers were used to solve the complex calculation in a neural network. Finally, the real and imaginary parts of the reconstructed signal were the outputs of the output layer. The optimal weights in this designed neural network were trained and optimized by comparing the output reconstructed signal with the target signal, and a stochastic gradient descent optimizer was used to update the weights of the network. Finally, the numerical examples and experimental examples of a cable model were employed to illustrate the ability of the proposed method. The results show that the proposed method which is called neural network–adaptive sparse time–frequency analysis can give accurate identification of the instantaneous frequency, and it has a better robustness to initial values when compared with adaptive sparse time–frequency analysis.

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

scholarly journals Time-Frequency Methods for Structural Health Monitoring

Sensors ◽  
2014 ◽  
Vol 14 (3) ◽  
pp. 5147-5173 ◽  
Author(s):  
Alexander Pyayt ◽  
Alexey Kozionov ◽  
Ilya Mokhov ◽  
Bernhard Lang ◽  
Robert Meijer ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Time Frequency ◽  
Structural Health

scholarly journals A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1825
Author(s):  
Marco Civera ◽  
Cecilia Surace
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Real Life ◽  
Adaptive Methods ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Time Frequency ◽  
Structural Health ◽  
Mode Decomposition ◽  
Depth Analysis

Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals.

Damage Classification Structural Health Monitoring in Bolted Structures Using Time-frequency Techniques

2009 ◽  
Vol 20 (11) ◽  
pp. 1289-1305 ◽  
Author(s):  
Debejyo Chakraborty ◽  
Narayan Kovvali ◽  
Jun Wei ◽  
Antonia Papandreou-Suppappola ◽  
Douglas Cochran ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Classification ◽  
Time Frequency ◽  
Structural Health
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