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Journal articles on the topic 'Signal processing; Spalling damage'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Jia, Shengxiang, and Ian Howard. "Comparison of localised spalling and crack damage from dynamic modelling of spur gear vibrations." Mechanical Systems and Signal Processing 20, no. 2 (February 2006): 332–49. http://dx.doi.org/10.1016/j.ymssp.2005.02.009.

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2

Shi, Luojie, Juan Wen, Baisong Pan, Yongyong Xiang, Qi Zhang, and Congkai Lin. "Dynamic Characteristics of a Gear System with Double-Teeth Spalling Fault and Its Fault Feature Analysis." Applied Sciences 10, no. 20 (October 11, 2020): 7058. http://dx.doi.org/10.3390/app10207058.

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Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor â of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.

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3

Merzoug, Mustapha, Khalid Ait-Sghir, Abdelhamid Miloudi, and Paul Jean Dron. "Early Diagnosis of Spalling in the Gear Teeth." Advanced Materials Research 1016 (August 2014): 249–55. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.249.

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The monitoring and vibratory analysis of gear transmission allow the prediction of a possible malfunction and breakdowns. As the gear transmission product non-stationary signals its treatment is too difficult with the usual tools of signal processing witch can product errors in its interpretation. As the characteristics of gear frequencies are predetermined, it is proposed to monitor (fault identification) using wavelet analysis. To simulate the signal to be analyzed, we intentionally introduced a spalling defect. We chose the Daubechies wavelet type which are the most used in diagnostic. The aim of this work is to try to control the various parameters related to the wavelet analysis for reliable and inexpensive detection, i.e., the order of the wavelet and level decomposition. The approach witch was previously used for bearings, consists on observing the kurtosis for several orders wavelet based on the default severity..

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4

Reddy, Mallikarjuna D., and Seetharaman Swarnamani. "Structural damage identification using signal processing method." International Journal of Advanced Structural Engineering 5, no. 1 (2013): 6. http://dx.doi.org/10.1186/2008-6695-5-6.

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5

Bhargav Sai, Cherukuri, and D. Mallikarjuna Reddy. "Dynamic Analysis of Faulty Rotors through Signal Processing." Applied Mechanics and Materials 852 (September 2016): 602–6. http://dx.doi.org/10.4028/www.scientific.net/amm.852.602.

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In this study, an effective method based on wavelet transform, for identification of damage on rotating shafts is proposed. The nodal displacement data of damaged rotor is processed to obtain wavelet coefficients to detect, localise and quantify damage severity. Because the wavelet coefficients are calculated with various scaled indices, local disturbances in the mode shape data can be found out in the finer scales that are positioned at local disturbances. In the present work the displacement data are extracted from the MATLAB model at a particular speed. Damage is represented as reduction in diameter of the shaft. The difference vectors between damaged and undamaged shafts are used as input vectors for wavelet analysis. The measure of damage severity is estimated using a parameter formulated from the distribution of wavelet coefficients with respect to the scales. Diagnosis results for different damage cases such as single and multiple damages are presented.

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6

Staszewski, W. J. "Intelligent signal processing for damage detection in composite materials." Composites Science and Technology 62, no. 7-8 (June 2002): 941–50. http://dx.doi.org/10.1016/s0266-3538(02)00008-8.

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7

Bochud, N., A. A. Fahim, Á. M. Gómez, and G. Rus. "Impact Damage Characterization in Composites Using Signal Processing Techniques." Procedia Engineering 14 (2011): 169–76. http://dx.doi.org/10.1016/j.proeng.2011.07.020.

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8

Bediaga, Inigo, Xabier Mendizabal, Aitor Arnaiz, and Jokin Munoa. "Ball bearing damage detection using traditional signal processing algorithms." IEEE Instrumentation & Measurement Magazine 16, no. 2 (April 2013): 20–25. http://dx.doi.org/10.1109/mim.2013.6495676.

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9

Tian, Ying, Cheng-gang He, Jie Zhang, Qi-yue Liu, and Wen-jian Wang. "Experimental study on the vibration characteristic responses on the surface damage of wheel materials." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 232, no. 9 (September 7, 2017): 1160–68. http://dx.doi.org/10.1177/1350650117730491.

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The objective of the study was to explore the vibration signal responses on the surface damage of wheel materials using a JD-1 wheel–rail simulation facility. Vibration signals were extracted using the methods of local mean decomposition and Hilbert envelope spectrum. The surface damage of wheel rollers varies with different tangential forces. The results indicate that the surface damage of wheel materials has a corresponding characteristic frequency under different tangential forces conditions. When surface cracks appear on the surface of wheel rollers, the characteristic frequency of wheel roller is about 1830 Hz. However, the characteristic frequencies are about 800 Hz and 73 Hz for peeling and spalling damage on wheel roller. Multifractal dimensions of the vibration signals quantificationally identify and distinguish the surface damage types of wheel rollers, which can provide a meaningful guidance for the subsequent online detection of surface damage of wheel materials.

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10

El youbi, F., S. Grondel, and J. Assaad. "Signal processing for damage detection using two different array transducers." Ultrasonics 42, no. 1-9 (April 2004): 803–6. http://dx.doi.org/10.1016/j.ultras.2004.01.070.

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11

Andreadis, Ioannis, Ioannis Tsiftzis, and Anaxagoras Elenas. "Intelligent Seismic Acceleration Signal Processing for Damage Classification in Buildings." IEEE Transactions on Instrumentation and Measurement 56, no. 5 (October 2007): 1555–64. http://dx.doi.org/10.1109/tim.2007.895620.

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12

Beheshti Aval, Seyed Bahram, Vahid Ahmadian, Mohammad Maldar, and Ehsan Darvishan. "Damage detection of structures using signal processing and artificial neural networks." Advances in Structural Engineering 23, no. 5 (November 10, 2019): 884–97. http://dx.doi.org/10.1177/1369433219886079.

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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.

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13

Qiao, Long, and Asad Esmaeily. "An Overview of Signal-Based Damage Detection Methods." Applied Mechanics and Materials 94-96 (September 2011): 834–51. http://dx.doi.org/10.4028/www.scientific.net/amm.94-96.834.

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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.

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14

Abdullah, S., M. Loman, and N. Jamaluddin. "Signal Processing Method for Evaluating Fatigue Damage in a Piping System." Journal of Applied Sciences 9, no. 13 (June 15, 2009): 2381–89. http://dx.doi.org/10.3923/jas.2009.2381.2389.

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15

Roy, Niranjan, and Ranjan Ganguli. "Helicopter rotor blade frequency evolution with damage growth and signal processing." Journal of Sound and Vibration 283, no. 3-5 (May 2005): 821–51. http://dx.doi.org/10.1016/j.jsv.2004.05.015.

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16

Montejo, Luis A. "Signal processing based damage detection in structures subjected to random excitations." Structural Engineering and Mechanics 40, no. 6 (December 25, 2011): 745–62. http://dx.doi.org/10.12989/sem.2011.40.6.745.

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17

Sultan, Mohamed Thariq Hameed, Azmin Shakrine M. Rafie, Noorfaizal Yidris, Faizal Mustapha, and Dayang Laila Majid. "Damage Classification in CFRP Laminates Using Principal Component Analysis (PCA) Approach." Applied Mechanics and Materials 225 (November 2012): 189–94. http://dx.doi.org/10.4028/www.scientific.net/amm.225.189.

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Signal processing is an important element used for identifying damage in any SHM-related application. The method here is used to extract features from the use of different types of sensors, of which there are many. The responses from the sensors are also interpreted to classify the location and severity of the damage. This paper describes the signal processing approaches used for detecting the impact locations and monitoring the responses of impact damage. Further explanations are also given on the most widely-used software tools for damage detection and identification implemented throughout this research work. A brief introduction to these signal processing tools, together with some previous work related to impact damage detection, are presented and discussed in this paper.

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18

Wu, Biao, Yong Huang, Xiang Chen, Sridhar Krishnaswamy, and Hui Li. "Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model." Structural Health Monitoring 16, no. 3 (August 29, 2016): 347–62. http://dx.doi.org/10.1177/1475921716665252.

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Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performance against noise. First, a Gabor dictionary is designed based on the information of the noisy signal. Each atom of this dictionary is a modulated Gaussian pulse. Then the robust sparse Bayesian learning technique is used to efficiently decompose the guided wave signal. After signal decomposition, a two-step matching scheme is proposed to extract meaningful waveforms for damage detection and localization. Results from numerical simulations and experiments on isotropic aluminum plate structures are presented to verify the effectiveness of the proposed approach in mode identification and signal de-noising for damage detection.

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19

Castro, Bruno A., Fabricio G. Baptista, José A. C. Ulson, Alceu F. Alves, Guilherme A. M. Clerice, Bruno A. Hernandez, and Fernando S. Campos. "Structural Damage Location by Low-Cost Piezoelectric Transducer and Advanced Signal Processing Techniques." Proceedings 4, no. 1 (November 14, 2018): 2. http://dx.doi.org/10.3390/ecsa-5-05725.

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The development of new low-cost transducers and systems has been extensively aimed at in both industry and academia to promote a correct failure diagnosis in aerospace, naval, and civil structures. In this context, structural health monitoring (SHM) engineering is focused on promoting human safety and a reduction in the maintenance costs of these components. Traditionally, SHM aims to detect structural damages at the initial stage, before it reaches a critical level of severity. Numerous approaches for damage identification and location have been proposed in the literature. One of the most common damage location techniques is based on acoustic waves triangulation, which stands out as an effective approach. This method uses a piezoelectric transducer as a sensor to capture acoustic waves emitted by cracks or other damage. Basically, the damage location is defined by calculating the difference in the time of arrival (TOA) of the signals. Although it may be simple, the detection of TOA requires complex statistical and signal processing techniques. Based on this issue, this work proposes the evaluation of a low-cost piezoelectric transducer to determine damage location in metallic structures by comparing two methodologies of TOA identification, the Hinkley criterion and the statistical Akaike criterion. The tests were conducted on an aluminum beam in which two piezoelectric transducers were attached at each end. The damage was simulated by pencil lead break (PLB) test applied at four different points of the specimen and the acoustic signals emitted by the damage were acquired and processed by Hinkley and Akaike criteria. The results indicate that, although both signal processing methodologies were able to determine the damage location, Akaike presented higher precision when compared to Hinkley approach. Moreover, the experimental results indicated that the low-cost piezoelectric sensors have a great potential to be applied in the location of structural failures.

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20

Dao, Phong B., and Wieslaw J. Staszewski. "Lamb wave based structural damage detection using cointegration and fractal signal processing." Mechanical Systems and Signal Processing 49, no. 1-2 (December 2014): 285–301. http://dx.doi.org/10.1016/j.ymssp.2014.04.011.

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21

Yu, Lingyu, and Victor Giurgiutiu. "Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors." Smart Structures and Systems 1, no. 2 (April 25, 2005): 185–215. http://dx.doi.org/10.12989/sss.2005.1.2.185.

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22

Taha, M. M. Reda, A. Noureldin, A. Osman, and N. El-Sheimy. "Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring." Canadian Journal of Civil Engineering 31, no. 5 (October 1, 2004): 719–31. http://dx.doi.org/10.1139/l04-022.

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This paper suggests the use of wavelet multiresolution analysis (WMRA) as a reliable tool for digital signal processing in structural health monitoring (SHM) systems. A damage occurrence detection algorithm using WMRA augmented with artificial neural networks (ANN) is described. The suggested algorithm allows intelligent monitoring of structures in real time. The probability of damage occurrence is determined by evaluating the wavelet norm index (WNI) representing the energy of a signal describing the change in the system dynamics due to damage. An example application of the proposed algorithm is presented using a finite element simulated structural dynamics of a prestressed concrete bridge. The new algorithm showed very promising results.Key words: structural health monitoring, neural networks, wavelet analysis, signal processing, damage detection.

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23

Figlus, Tomasz. "A Method for Diagnosing Gearboxes of Means of Transport Using Multi-Stage Filtering and Entropy." Entropy 21, no. 5 (April 27, 2019): 441. http://dx.doi.org/10.3390/e21050441.

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The paper presents a method of processing vibration signals which was designed to detect damage to wheels of gearboxes for means of transport. This method uses entropy calculation, and multi-stage filtering calculated by means of digital filters and the Walsh–Hadamard transform to process signals. The presented method enables the extraction of vibration symptoms, which are symptoms of gear damage, from a complex vibration signal of a gearbox. The combination of multi-stage filtering and entropy enables the elimination of fast-changing vibration impulses, which interfere with the damage diagnosis process, and make it possible to obtain a synthetic signal that provides information about the state of the gearing. The paper demonstrates the usefulness of the developed method in the diagnosis of a gearbox in which two types of gearing damage were simulated: tooth chipping and damage to the working surface of the teeth. The research shows that the application of the proposed method of vibration of signal processing enables observation of the qualitative and quantitative changes in the entropy signal after denoising, which are unambiguous symptoms of the diagnosed damage.

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24

Morlier, Joseph, F. Bos, and P. Castera. "Benchmark of Damage Localisation Algorithms Using Mode Shape Data." Key Engineering Materials 293-294 (September 2005): 305–12. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.305.

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This paper presents a comparative study of three enhanced signal processing methods to locate damage on mode shape data. The first method called curvature mode shape is used as a reference. The second tool uses wavelet transform and singularity detection theory to locate damage. Finally we introduce the windowed fractal dimension of a signal as a tool to easily measure the local complexity of a signal. Our benchmark aims at comparing the crack detection using optimal spatial sampling under different severity, beam boundary conditions (BCs) and added noise measurements.

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25

Makowski, Ryszard, and Radoslaw Zimroz. "Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery." Key Engineering Materials 588 (October 2013): 214–22. http://dx.doi.org/10.4028/www.scientific.net/kem.588.214.

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The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

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26

Zhong, Zhen, Baoju Zhang, Tariq S. Durrani, and Shuifang Xiao. "Nonlinear signal processing for vocal folds damage detection based on heterogeneous sensor network." Signal Processing 126 (September 2016): 125–33. http://dx.doi.org/10.1016/j.sigpro.2015.08.019.

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27

Daponte, P., F. Maceri, and R. S. Olivito. "Ultrasonic signal-processing techniques for the measurement of damage growth in structural materials." IEEE Transactions on Instrumentation and Measurement 44, no. 6 (1995): 1003–8. http://dx.doi.org/10.1109/19.475146.

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28

Naderpour, Hosein, Amir Ezzodin, Ali Kheyroddin, and Gholamreza Ghodrati Amiri. "Signal processing based damage detection of concrete bridge piers subjected to consequent excitations." Journal of Vibroengineering 19, no. 3 (May 15, 2017): 2080–89. http://dx.doi.org/10.21595/jve.2015.16474.

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29

Liu, Jing-Liang, Si-Fan Wang, Jin-Yang Zheng, Chia-Ming Chang, Xiao-Jun Wei, and Wei-Xin Ren. "Time–Frequency Signal Processing for Integrity Assessment and Damage Localization of Concrete Piles." International Journal of Structural Stability and Dynamics 20, no. 02 (December 23, 2019): 2050020. http://dx.doi.org/10.1142/s0219455420500200.

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This paper presents a new integrity assessment and damage localization method for piles based on one-dimensional wave propagation theory by integrating the analytical mode decomposition (AMD), recursive Hilbert transform (RHT) and complex continuous wavelet transform (CCWT) into a single assessment tool. The AMD is first used as a band pass filter to extract the mono-component over a frequency band of interest from the response of a pile head, aimed at attenuating the interference from various noisy signals. Then, the mono-component signal is demodulated into a purely frequency-modulated signal by means of RHT, which greatly reduces the interferences from the amplitude-modulated function. Finally, the CCWT is utilized to process the frequency-modulated signal and to calculate phase angles; the latter are subsequently mapped into the time–frequency domain to localize pile damage. The methodology is verified by a numerical example, in which a concrete pile is modeled by the finite element method considering the soil-pile interaction, and by an experimental case study on an actual pile. The results from the numerical and experimental examples demonstrate that the proposed method improves the efficiency of damage identification when compared with other three methods ([Formula: see text], [Formula: see text] and CCWT). In addition, the proposed method enables the localization of damage in full-scale piles situated in soil with an acceptable engineering accuracy by mutual validation with other pile integrity assessment methods, e.g. the ultrasonic emission method.

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30

Bajric, Rusmir, Ninoslav Zuber, Georgios Alexandros Skrimpas, and Nenad Mijatovic. "Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox." Shock and Vibration 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6748469.

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Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

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31

Xue, Gang. "Damage Detection of Reinforced Concrete Beams by Wavelet Analysis." Applied Mechanics and Materials 166-169 (May 2012): 1416–21. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1416.

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Simple-supported reinforced concrete (R.C) beams are subjected to an increasing static load in the middle to introduce cracks, and the last load step corresponds to failure. After each load step and unloading, an experimental dynamic monitoring is performed. Multiple degree freedom dynamic equation of the test beams is wavelet transformed, and structural dynamic response expression on multi-scale is acquired. It’s shown that multi-scale decomposition of signal comprises more structural damage information. By means of DASP signal processing system, binary wavelet transformation is applied to dynamic signal of reinforced concrete beams on different damage state. Through analyzing wave-figure of all frequency scales, the damage of reinforced concrete beams is detected.

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32

Wandowski, Tomasz, Pawel Malinowski, and Wiesław M. Ostachowicz. "Damage Localisation in Thin Panels Using Elastic Wave Propagation Method." Key Engineering Materials 413-414 (June 2009): 87–93. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.87.

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In this paper algorithm for damage localisation in thin panels made of aluminium alloy has been proposed. Mentioned algorithm uses Lamb wave propagation methods and geometrical approach for damage localisation. Elastic waves are generated and received using piezoelectric transducers. Excited elastic waves propagate and reflect from panel boundary and discontinuities existing in the panel. Wave reflection can be registered through the piezoelectric transducers and used in signal processing algorithm. Processing algorithm consists of two parts: signal filtering and extraction of damage location. The first part is used in order to remove noise from received signals. Second part allows to extract arrival time of waves reflected from discontinuity, very often called Time Of Flight (TOF). Localisation algorithm uses pairs of transducers from a concentrated transducers configuration. Using signals from pair of transducers two times of reflection can be extracted from received signals. Because coordinates of transducers are well known ellipse can be constructed based on extracted times of waves reflections. Damage lies one ellipse but it is not known exactly where. Therefore one ellipse is not enough to localise a discontinuity. In order of proper damage localisation more ellipses must be used. In this purpose signals received by larger number of transducers pairs are used in damage localisation algorithm. Points of ellipses intersections allow to indicate localisation of damage. Described signal processing algorithm has been coded in the MATLAB® environment. In this work experimental results has been presented.

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33

Dziendzikowski, Michal, Krzysztof Dragan, Artur Kurnyta, Sylwester Klysz, and Andrzej Leski. "Health Monitoring of the Aircraft Structure during a Full Scale Fatigue Test with Use of an Active Piezoelectric Sensor Network." Solid State Phenomena 220-221 (January 2015): 328–32. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.328.

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The paper presents an approach to develop a system for fatigue crack growth monitoring and early damage detection in the PZL – 130 ORLIK TC II turbo-prop military trainer aircraft structure. The system functioning is based on elastic waves propagation excited in the structure by piezoelectric PZT transducers. In the paper, a built block approach for the system design, signal processing as well as damage detection is presented. Description of damage detection capabilities are delivered in the paper and some issues concerning the proposed signal processing methods and their application to crack growth estimation models are discussed. Selected preliminary results obtained during the Full Scale Fatigue Test thus far are also presented.

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34

Mizutani, Tsukasa, Nagisa Nakamura, Takahiro Yamaguchi, Minoru Tarumi, Yusuke Ando, and Ikuo Hara. "Bridge Slab Damage Detection by Signal Processing of UHF-Band Ground Penetrating Radar Data." Journal of Disaster Research 12, no. 3 (May 29, 2017): 415–21. http://dx.doi.org/10.20965/jdr.2017.p0415.

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Maintenance costs for infrastructure, such as bridges, have been increasing particularly in the developed countries. Bridge slabs are important parts of bridges; however, the evaluation of their structural conditions requires significant manpower and time because dense hammering tests have to be conducted as part of the present inspection methods. To overcome this difficulty, a non-contact inspection technique using a radar is focused in this research. Radar techniques are typically utilized in the fields of mine-search, oil-source search, and geographical archeology. However, these searches are conducted by only visually checking reflected-wave images, and thus, the evaluation strongly depends on the abilities and expertise of the inspectors. To more effectively utilize these radar techniques for evaluating a bridge slab condition, analysis of the reflected wave signals should be made automatic, fast, and objective because the number of bridges to be inspected is large. In this research, to detect the damages on a slab, some signal processing techniques for measuring the reflected wave signal by a UHF-band fast scanning and non-contact radar are proposed, and their validity is shown by applying them to the signals obtained from full-scale bridge slab models in which certain ideal damages are embedded.

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35

Mizutani, Tsukasa, Nagisa Nakamura, Takahiro Yamaguchi, Minoru Tarumi, and Yusuke Ando. "Signal Processing for Fast RC Bridge Slab Damage Detection by Using UHF-band Radar." Procedia Engineering 188 (2017): 377–84. http://dx.doi.org/10.1016/j.proeng.2017.04.498.

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36

Michaels, Jennifer E., and Thomas E. Michaels. "Guided wave signal processing and image fusion for in situ damage localization in plates." Wave Motion 44, no. 6 (June 2007): 482–92. http://dx.doi.org/10.1016/j.wavemoti.2007.02.008.

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37

Huang, Yong, Changsong Shao, and Xin Yan. "Fractal signal processing method of acoustic emission monitoring for seismic damage of concrete columns." International Journal of Lifecycle Performance Engineering 3, no. 1 (2019): 59. http://dx.doi.org/10.1504/ijlcpe.2019.099894.

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Yan, Xin, Yong Huang, and Changsong Shao. "Fractal signal processing method of acoustic emission monitoring for seismic damage of concrete columns." International Journal of Lifecycle Performance Engineering 3, no. 1 (2019): 59. http://dx.doi.org/10.1504/ijlcpe.2019.10021481.

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39

Ahmadi, Hassan, Sajad Esmaielzadeh, and Seyed Abbas Hosseini. "Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations." Journal of Vibroengineering 21, no. 8 (December 31, 2019): 2196–215. http://dx.doi.org/10.21595/jve.2019.20202.

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KAWADA, Masato, Kazuteru NAGAMURA, Kiyotaka IKEJO, Mitsuo HASHIMOTO, and Akimasa YAMAMOTO. "2105 Damage Diagnosis of Gear Tooth Surface by Synchronous Averaging Processing of Vibration Signal." Proceedings of the Symposium on Motion and Power Transmission 2013 (2013): 133–37. http://dx.doi.org/10.1299/jsmempt.2013.133.

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41

Tian, Jie, Hongyao Wang, Junying Zhou, and Guoying Meng. "Study of pre-processing model of coal-mine hoist wire-rope fatigue damage signal." International Journal of Mining Science and Technology 25, no. 6 (November 2015): 1017–21. http://dx.doi.org/10.1016/j.ijmst.2015.09.021.

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42

Li, Zhi Xiong, Xin Ping Yan, Cheng Qing Yuan, and Li Li. "Gear Multi-Faults Diagnosis of a Rotating Machinery Based on Independent Component Analysis and Fuzzy K-Nearest Neighbor." Advanced Materials Research 108-111 (May 2010): 1033–38. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1033.

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Gearboxes are extensively used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the gearbox are mostly caused by the gear failures. It is therefore crucial for engineers and researchers to monitor the gear conditions in time in order to prevent the malfunctions of the plants. In this paper, a condition monitoring and faults identification technique for rotating machineries based on independent component analysis (ICA) and fuzzy k-nearest neighbor (FKNN) is described. In the diagnosis process, the ICA was initially employed to separate characteristic vibration signal and interference vibration signal from the parallel time series obtained from multi-channel accelerometers mounted on different positions of the gearbox. The wavelet transform (WT) and autoregressive (AR) model method then were performed as the feature extraction technique to attain the original feature vector of the characteristic signal. Meanwhile, the ICA was used again to reduce the dimensionality of the original feature vector. Hence, the useless information in the feature vector could be removed. Finally, the FKNN algorithm was implemented in the pattern recognition process to identify the conditions of the gears of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the ICA processing, and the proposed diagnostic system is effective for the gear multi-faults diagnosis, including the gear crack failure, pitting failure, gear tooth broken, compound fault of wear and spalling, etc. In addition, the proposed method can achieve higher performance than that without ICA processing with respect to the classification rate.

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Shrestha, Manish Man, Bibek Ropakheti, Uddhav Bhattarai, Ajay Adhikari, and Shreeram Thakur. "Intelligent Wireless Ultrasonic Device for Damage Detection of Metallic Structures." Scientific World 14, no. 14 (February 15, 2021): 31–36. http://dx.doi.org/10.3126/sw.v14i14.34979.

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In today’s world, it is necessary to monitor structures for possible damages. A failure to monitor the structures properly can cause structural catastrophe. Many researchers have worked on the low-power ultrasonic device to monitor the structures. In this research, we present an intelligent ultrasonic device (IUD) to monitor and detect the damages on the structures. The device uses microcontroller, actuator interface circuit, sensor interface circuit and radio frequency (RF) modem. The microcontroller has in-built high-speed analog-to-digital converter (ADC), digital-to-analog converter (DAC) and floating-point unit for signal processing. The controller generates the tone-burst signal and sends it to actuator interface circuit. The actuator interface circuit conditions the received signal from the microcontroller and drives the Piezoelectric Transducer (PZT) actuator. The actuator generates an ultrasonic wave in the structure. The wave is then sensed by PZT sensors. The sensor interface circuit selects the signal from desired PZT sensor and sends it to the microcontroller for further processing. The microcontroller digitizes the signal and computes the damage index and only if the damage is severe, it will send data wirelessly to the nearby PC. To test the device, iron specimen was prepared, PZT actuator and PZT sensor was mounted on it. The artificial crack was then induced on the specimen. The ultrasonic wave was then collected from the structure. By analyzing the ultrasonic wave, the device successfully detected the induced crack in the structure. The future work will be to use GSM modem so that the device can be monitored in the real time from the remote location.

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Pourrastegar, Azita, Hesham Othman, and Hesham Marzouk. "Vibration-based damage identification for reinforced concrete slab-type structures using fiber-optic sensors and random decrement technique." RILEM Technical Letters 4 (May 8, 2020): 163–71. http://dx.doi.org/10.21809/rilemtechlett.2019.103.

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This paper presents and evaluates a damage identification system for reinforced concrete (RC) slab-type structures based on non-destructive vibration testing, Random decrement (RD) signal processing technique, and embedded smart network of fiber-optic sensors. The proposed system aims to overcome the challenges associated with the use of electrical sensors and signal processing of noisy dynamic data. Two experimental modal analysis investigations have been conducted. First modal testing focuses on investigating the capability of fiber-optic sensors and Multi-channel random decrement (MCRD) processing technique to locate damage in RC slabs through changes in the first mode shape response with damage. The second modal testing focuses on the detection of damage intensity using the RD technique through the change in frequency and damping dynamic parameters. The results show that RD technique can be used effectively to extract the free vibration response of RC slab-type structures; fiber-optic sensors are more sensitive to capture damage severity in comparison to electrical accelerometer sensors, especially, at steel yielding and failure load; MCRD technique can be used effectively to generate mode shapes for RC slabs based on fiber-optic grating FBG sensors measurements. On the other hand, electrical strain gauges were noisy and it was difficult to obtain any measurable data; A damage identification system based on non-destructive vibration testing, MCRD processing technique, and using an embedded smart network of fiber-optic sensors can estimate accurately the damage location through changes in the first mode shape.

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Kerle, N., F. Nex, D. Duarte, and A. Vetrivel. "UAV-BASED STRUCTURAL DAMAGE MAPPING – RESULTS FROM 6 YEARS OF RESEARCH IN TWO EUROPEAN PROJECTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 21, 2019): 187–94. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-187-2019.

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<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

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Xu, Baochun, Mulan Wang, Peijuan Li, Qihua Cheng, and Yunlong Sheng. "Application of Instantaneous Parameter Characteristic in Active Lamb Wave Based Monitoring of Plate Structural Health." Applied Sciences 10, no. 16 (August 14, 2020): 5664. http://dx.doi.org/10.3390/app10165664.

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In the active Lamb wave based monitoring of plate structural health, it is difficult to extract damage information from active Lamb waves based on single sensor collection. Based on the Hilbert–Huang transform (HHT) instantaneous processing method, this paper proposes to study the damage information carried by a single sensor monitoring signal from the instantaneous parameter characteristics of the signal. The instantaneous phase change caused by the phase difference between the damage scattering wave and the direct wave is studied. The change of the marginal spectrum amplitude in the effective range caused by the damage scattering wave is studied in continuous multiple frequency bands. Finally, the damage information extraction based on a single sensor monitoring signal is realized. From the model analysis and experimental results, it is reliable and feasible to realize the active Lamb wave based monitoring of plate structural health according to the instantaneous parameter change characteristics from a single sensor signal.

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Antoniadou, Ifigeneia, Nikolaos Dervilis, Robert J. Barthorpe, Graeme Manson, and Keith Worden. "Advanced Tools for Damage Detection in Wind Turbines." Key Engineering Materials 569-570 (July 2013): 547–54. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.547.

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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.

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Li, Hong Yuan, and Hong Xu. "Damage Detection for Structural Health Monitoring Using Ultrasonic Guided Waves." Key Engineering Materials 525-526 (November 2012): 433–36. http://dx.doi.org/10.4028/www.scientific.net/kem.525-526.433.

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The use of ultrasonic guided waves for damage detection suffers from the multi-modes and dispersion. Much attention has been paid to transducer design and excitation frequency chosen to suppress the multiple modes and dispersion. However, little attention has been paid to complex signal processing. In this paper, the dispersive propagation of the guided waves are firstly reviewed. And then the matching pursuit method is introduced as a feature extraction algorithm. In order to present well the characteristic of the guided waves signal, a dispersive dictionary is designed based on the guided waves propagation. A two-stage pursuit method consisted of coarse and fine matching is used. At last, the proposed method is verified by finite element simulation and successfully extracted damage related dispersive pulses from measured noisy signal.

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Liu, Ning, and Thomas Schumacher. "Improved Denoising of Structural Vibration Data Employing Bilateral Filtering." Sensors 20, no. 5 (March 5, 2020): 1423. http://dx.doi.org/10.3390/s20051423.

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With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

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Nazarko, Piotr, and Leonard Ziemianski. "Damage detection in aluminum and composite elements using neural networks for Lamb waves signal processing." Engineering Failure Analysis 69 (November 2016): 97–107. http://dx.doi.org/10.1016/j.engfailanal.2016.07.001.

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