An Overview of Signal-Based Damage Detection Methods

2011 ◽  
Vol 94-96 ◽  
pp. 834-851 ◽  
Author(s):  
Long Qiao ◽  
Asad Esmaeily
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Feature Extraction ◽  
Damage Detection ◽  
Structural Response ◽  
Nonlinear Behavior ◽  
Detection Methods ◽  
Time Frequency ◽  
Frequency Domain Methods ◽  
Feature Extraction And Selection

Deterioration of structures due to aging, cumulative crack growth or excessive response significantly affects the performance and safety of structures during their service life. Recently, signal-based methods have received many attentions for structural health monitoring and damage detection. These methods examine changes in the features derived directly from the measured time histories or their corresponding spectra through proper signal processing methods and algorithms to detect damage. Based on different signal processing techniques for feature extraction, these methods are classified into time-domain methods, frequency-domain methods, and time-frequency (or time-scale)-domain methods. As an enhancement for feature extraction, selection and classification, pattern recognition techniques are deeply integrated into signal-based damage detection. This paper provided an overview of these methods based on two aspects: (1) feature extraction and selection, and (2) pattern recognition. Signal-based methods are particularly more effective for structures with complicated nonlinear behavior and the incomplete, incoherent, and noise-contaminated measurements of structural response.

Advanced Tools for Damage Detection in Wind Turbines

2013 ◽  
Vol 569-570 ◽  
pp. 547-554
Author(s):  
Ifigeneia Antoniadou ◽  
Nikolaos Dervilis ◽  
Robert J. Barthorpe ◽  
Graeme Manson ◽  
Keith Worden
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Damage Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Recent Time ◽  
Time Frequency

The paper summarises some advanced damage detection approaches used for Structural Health Monitoring (SHM) and Condition Monitoring (CM) of wind turbine systems. In the signal processing part, recent time-frequency analysis methods will be presented and examples of their application on condition monitoring of gearboxes will be given. In the pattern recognition part, examples of damage detection in blades will be used to introduce different algorithms for novelty detection.

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.

Damage Assessment of a Building Subjected to a Terrorist Attack

2014 ◽  
Vol 17 (11) ◽  
pp. 1693-1704 ◽  
Author(s):  
E.L. Eskew ◽  
S. Jang
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Numerical Models ◽  
Terrorist Attack ◽  
Structural Response ◽  
Structural Condition ◽  
Condition Assessment ◽  
Detection Methods ◽  
Specific Element

An increasing threat of global terrorism has led to concerns about bombings of buildings, which could cause minor to severe structural damage. After such an event, it is important to rapidly assess the damage to the building to ensure safe and efficient emergency response. Current methods of visual inspection and non-destructive testing are expensive, subjective, and time consuming for emergency responders' usage immediately after an attack. On the other hand, vibration-based damage detection methods with wireless smart sensors could provide rapid assessment of structural characteristics with low cost. For blast analysis, structural response is usually determined using a simplified SDOF version of the undamaged structure, such as used in a Pressure-Impulse (P-I) Diagram, or using more complex FEM (finite element method) models. However, the simplified models cannot take into account damage caused by blast focus at a specific location or on a specific element, which may induce local failure leading to potential progressive collapse, and the more complex FEM models take too long to derive applicable results to be effective for a rapid structural assessment. In this paper, a new method to incorporate vibration-based damage detection methods to calculate the multi degree of freedom structural stiffness for determining structural condition is provided to create a framework for the rapid structural condition assessment of buildings after a terrorist attack. The stiffness parameters are generated from the modal analysis of the measured vibration on the building, which are then used in a numerical simulation to determine its structural response from the blast. The calculated structural response is then compared to limit conditions that have been developed from ASCE blast design codes to determine the damage assessment. A laboratory-scale building frame has been employed to validate the developed use of experimentally determined stiffness by comparing the P-I diagram using the experimental stiffness with that from numerical models. The reasonable match between the P-I diagrams from the numerical models and the experiments shows the positive potential of the method. The framework and examples of how to develop a rapid condition assessment are presented.

scholarly journals Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Peng-yuan Liu ◽  
Bing Li ◽  
Cui-e Han ◽  
Feng Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Operating Conditions ◽  
Nsga Ii ◽  
Selection Scheme ◽  
Time Frequency ◽  
Feature Extraction And Selection

A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, theStransform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.

Damage detection of structures using signal processing and artificial neural networks

2019 ◽  
Vol 23 (5) ◽  
pp. 884-897 ◽  
Author(s):  
Seyed Bahram Beheshti Aval ◽  
Vahid Ahmadian ◽  
Mohammad Maldar ◽  
Ehsan Darvishan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Processing ◽  
Damage Detection ◽  
Empirical Mode Decomposition ◽  
Degree Of Freedom ◽  
Benchmark Problem ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Artificial Neural

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.

scholarly journals Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1790
Author(s):  
Zi Zhang ◽  
Hong Pan ◽  
Xingyu Wang ◽  
Zhibin Lin
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Damage Detection ◽  
Lamb Wave ◽  
Lamb Waves ◽  
Feature Selection Method ◽  
Support Vector ◽  
Time Frequency ◽  
Damage States

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.

Voice Recognition from Spectrograms: A Wavelet Based Approach

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 165-172 ◽  
Author(s):  
G. van de Wouwer ◽  
P. Scheunders ◽  
D. van Dyck ◽  
M. de Bodt ◽  
F. Wuyts ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Accuracy ◽  
Voice Recognition ◽  
Original Signal ◽  
Pattern Recognition Technique ◽  
Time Frequency ◽  
Novel Method ◽  
Recognition Technique

The performance of a pattern recognition technique is usually determined by the ability of extracting useful features from the available data so as to effectively characterize and discriminate between patterns. We describe a novel method for feature extraction from speech signals. For this purpose, we generate spectrograms, which are time-frequency representations of the original signal. We show that, by considering this spectrogram as a textured image, a wavelet transform can be applied to generate useful features for recognizing the speech signal. This method is used for the classification of voice dysphonia. Its performance is compared with another technique taken from the literature. A recognition accuracy of 98% is achieved for the classification between normal an dysphonic voices.

Vibration-based experimental damage detection of a small-scale wind turbine blade

2016 ◽  
Vol 16 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Yaowen Ou ◽  
Eleni N Chatzi ◽  
Vasilis K Dertimanis ◽  
Minas D Spiridonakos
Keyword(s):  
Damage Detection ◽  
Wind Turbine ◽  
Hypothesis Test ◽  
Structural Response ◽  
Life Cycle Management ◽  
Detection Methods ◽  
Statistical Hypothesis ◽  
Small Scale ◽  
Statistical Hypothesis Test ◽  
Real Time Information

Structural health monitoring offers an attractive tool for condition assessment, fault prognosis and life-cycle management of wind turbine components. However, owing to the intense loading conditions, geometrical nonlinearities, complex material properties and the lack of real-time information on induced structural response, damage detection and characterization of structural components comprise a challenging task. This study is focused on the problem of damage detection for a small-scale wind turbine (Sonkyo Energy Windspot 3.5 kW) experimental blade. To this end, the blade is dynamically tested in both its nominal (healthy) condition and for artificially induced damage of varying types and intensities. The response is monitored via a set of accelerometers; the acquired signals serve for damage detection via the use of appropriate statistical and modal damage detection methods. The former rely on extraction of a characteristic statistical quantity and establishment of an associated statistical hypothesis test, while the latter rely on tracking of damage-sensitive variations of modal properties. The results indicate that statistical-based methods outperform modal-based ones, succeeding in the detection of induced damage, even at low levels.

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