Matrix factorization to time-frequency distribution for structural health monitoring

2016 ◽  
Author(s):  
Chia-Ming Chang ◽  
Shieh-Kung Huang
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Distribution ◽  
Health Monitoring ◽  
Matrix Factorization ◽  
Time Frequency ◽  
Structural Health ◽  
Time Frequency Distribution

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 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.

scholarly journals A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 445
Author(s):  
Huaqing Wang ◽  
Mengyang Wang ◽  
Junlin Li ◽  
Liuyang Song ◽  
Yansong Hao
Keyword(s):  
Frequency Distribution ◽  
Matrix Factorization ◽  
Vibration Signal ◽  
Separation Method ◽  
Signal Separation ◽  
Reference Vector ◽  
Time Frequency ◽  
Fault Features ◽  
Time Frequency Distribution ◽  
Non Negative Matrix Factorization

In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time–frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time–frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.

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.

Investigation on the data augmentation using machine learning algorithms in structural health monitoring information

2021 ◽  
pp. 147592172199623
Author(s):  
Xuyan Tan ◽  
Xuanxuan Sun ◽  
Weizhong Chen ◽  
Bowen Du ◽  
Junchen Ye ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Supervised Learning ◽  
Monitoring System ◽  
Health Monitoring ◽  
Matrix Factorization ◽  
Data Augmentation ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
Non Negative Matrix Factorization

Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various forms of abnormal conditions. Along this line, this article proposed an integrated framework for data augmentation in structural health monitoring system using machine learning algorithms. As a case study, the monitoring data obtained from structural health monitoring system in the Nanjing Yangtze River Tunnel are selected to make experience. First, the original data are reconstructed based on an improved non-negative matrix factorization model to detect abnormal conditions occurred in different cases. Subsequently, multiple supervised learning methods are introduced to process the abnormal conditions detected by non-negative matrix factorization. The effectiveness of multiple supervised learning methods at different missing ratios is discussed to improve its university. The experimental results indicate that non-negative matrix factorization can recognize different abnormal situations simultaneously. The supervised learning algorithms expressed good effects to impute datasets under different missing rates. Therefore, the presented framework is applied to this case for data augmentation, which is crucial for further analysis and provides an important reference for similar projects.

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.

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

Ultrasonic guided wave propagation in composites including damage using high-fidelity local interaction simulation

2017 ◽  
Vol 29 (5) ◽  
pp. 969-985 ◽  
Author(s):  
Guoyi Li ◽  
Rajesh Kumar Neerukatti ◽  
Aditi Chattopadhyay
Keyword(s):  
Numerical Simulation ◽  
Wave Propagation ◽  
Composite Materials ◽  
Structural Health Monitoring ◽  
Simulation Model ◽  
Health Monitoring ◽  
Local Interaction ◽  
Time Frequency ◽  
Structural Health ◽  
Interaction Simulation

Composite materials are used in many advanced engineering applications because of high specific strength and stiffness. Their complex damage mechanisms and failure modes, however, are still not well-understood, thus challenging the application safety. Ultrasonic guided waves are promising structural health monitoring tools used to determine the operational safety of composite materials. In this article, a fully coupled numerical simulation model is used to study wave propagation and dispersion in composites under varying sensor locations, propagating orientations, excitation frequencies, and damage locations. The model is based on the local interaction simulation approaches/sharp interface model wherein output sensor signals are processed using the matching pursuit decomposition algorithm to study the signal features in the time–frequency domain. The changes in signals due to varying damage locations with respect to the through-thickness direction are studied under anti-symmetrical and symmetrical excitation scenarios. The results show that the signal from symmetric excitation is more sensitive to the damage location, while the signal from anti-symmetric excitation is less dispersive. It indicates that comprising effective feature extraction technique with the accurate physics-based numerical simulation model can be implemented to develop robust structural health monitoring framework for composites.

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