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3 pages, 158 KiB

Open AccessEditorial

Special Feature Vibration-Based Structural Health Monitoring

by Junhong Park

Appl. Sci. 2020, 10(15), 5139; https://doi.org/10.3390/app10155139 - 27 Jul 2020

Cited by 5 | Viewed by 2150

Abstract

Structural health monitoring by vibration requires the understanding of multidisciplinary fields of engineering sciences [...] Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

Research

Jump to: Editorial, Review, Other

15 pages, 1991 KiB

Open AccessArticle

Modal Analysis of Selected Measuring Bases and Their Impact on the Recorded Level of Surface Accelerations

by Krzysztof Robert Czech

Appl. Sci. 2020, 10(9), 3128; https://doi.org/10.3390/app10093128 - 30 Apr 2020

Cited by 3 | Viewed by 5950

Abstract

In predicting the impact of vibrations propagated in a ground on newly designed construction objects, it is extremely important to reliably measure the time histories of velocity or acceleration at the site of the planned investment. As some studies show, the method of [...] Read more.

In predicting the impact of vibrations propagated in a ground on newly designed construction objects, it is extremely important to reliably measure the time histories of velocity or acceleration at the site of the planned investment. As some studies show, the method of coupling accelerometers and geophones to the ground can affect not only the level of peak particle accelerations (PPA) or peak particle velocities (PPV)—commonly used in this type of evaluation—but also vibration frequency distribution of recorded signals. This makes it difficult to compare and analyze the results obtained by various research teams. Conclusions based on this type of comparison may be wrong. For this reason, it is extremely important that reliable dynamic measurements related to the propagation of ground surface vibrations should be carried out using not only appropriately selected measuring equipment (with the required sensitivity and measurement ranges—both in the time domain and frequency domain), but also the measurement bases/setups used for mounting various types of transducers, whose natural frequencies will be outside the frequency range relevant to the possible impact of vibrations on buildings and human beings inside. The paper presents the results of modal analyses carried out with the use of Ansys Engineering Simulation and 3D Design Software (based on the Finite Element Method) for three different types of measuring bases used to coupling accelerometers to the ground. Measuring bases selected for numerical analyses were in the form of a stiff steel spike of an X-shaped cross section (a measuring base No. 1) and two steel spikes of L-shaped cross-sections (a measuring bases No. 2 and No. 3). In the places of screwed accelerometers (three different types of IEPE/ICP transducers of varying sensitivity and a weight) point masses were attached to the measuring bases. As a result of the analyses, it was possible to determine the impact of individual methods of coupling of accelerometers to the ground on the reliability of recorded ground surface accelerations. Among others it was concluded that in each analyzed case the lowest natural frequencies of the measuring bases with attached accelerometers significantly exceeded 100 Hz. The widest frequency band free of resonance vibrations can be provided by the measuring base No. 3 (L50 × 50 × 5). Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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20 pages, 6680 KiB

Open AccessArticle

Structural Responses of a Supertall Building Subjected to a Severe Typhoon at Landfall

by Zhi Li, Jiyang Fu, Yuncheng He, Zhen Liu, Jiurong Wu, Rui Rao and Ching-Tai Ng

Appl. Sci. 2020, 10(8), 2965; https://doi.org/10.3390/app10082965 - 24 Apr 2020

Cited by 13 | Viewed by 3037

Abstract

Typhoon Mangkhut (1822) was one of the strongest tropical cyclones that ever impacted the south coast of China in past decades. During the passage of this typhoon, the structural health monitoring (SHM) system installed on a 303 m high building in this region [...] Read more.

Typhoon Mangkhut (1822) was one of the strongest tropical cyclones that ever impacted the south coast of China in past decades. During the passage of this typhoon, the structural health monitoring (SHM) system installed on a 303 m high building in this region worked effectively, and high-quality field measurements at nine height levels of the building were collected successfully, which provides a valuable opportunity to explore the dynamic properties of the building and the associated wind effects. In this study, the typhoon wind characteristics are presented first based on in-situ measurements at two sites. Acceleration responses of the building is then investigated, and the building’s serviceability is assessed against several comfort criteria. This study further focuses on the identification of modal parameters (i.e., natural frequency, damping ratio, and modal shape) via two methods: stochastic subspace identification (SSI) method and a method based on combined use of spectral analysis and random decrement technique (RDT). The good agreement between the two results demonstrates the effectiveness and the accuracy of the adopted methods. The obtained results are further compared with the stipulations in several technical codes as well as simulation results via finite element method to examine their performances in this real case. The amplitude dependence of natural frequencies and damping ratios of the studied building are also stressed. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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21 pages, 8987 KiB

Open AccessArticle

Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab

by Zhenpeng Wang, Minshui Huang and Jianfeng Gu

Appl. Sci. 2020, 10(8), 2869; https://doi.org/10.3390/app10082869 - 21 Apr 2020

Cited by 20 | Viewed by 2905

Abstract

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) [...] Read more.

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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14 pages, 3104 KiB

Open AccessArticle

Minimal Model for Sprag-Slip Oscillation as Catastrophe-Type Behavior

by Jaeyoung Kang and Jaehyeon Nam

Appl. Sci. 2020, 10(8), 2748; https://doi.org/10.3390/app10082748 - 16 Apr 2020

Cited by 4 | Viewed by 2223

Abstract

The infinite spragging force can be produced by a spring inclined with a constant angle in a frictional sliding system. The ensuing oscillation is called the sprag-slip oscillation. This sprag-slip oscillation is re-examined by using the minimal nonlinear dynamic model with the variable [...] Read more.

The infinite spragging force can be produced by a spring inclined with a constant angle in a frictional sliding system. The ensuing oscillation is called the sprag-slip oscillation. This sprag-slip oscillation is re-examined by using the minimal nonlinear dynamic model with the variable angle of the inclined spring. Nonlinear equilibrium equation is converted into the novel polynomial form. This simple but more realistic sprag model shows that the infinite spragging force is not realistic and the catastrophic static deformation in the steady-sliding state can occur. It indicates that the ‘sprag’, termed by Spurr, can be described by this catastrophic characteristic of the frictional sliding system. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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11 pages, 2631 KiB

Open AccessArticle

Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm

by Wootae Jeong and Dahae Jeong

Appl. Sci. 2020, 10(6), 2110; https://doi.org/10.3390/app10062110 - 20 Mar 2020

Cited by 6 | Viewed by 3295

Abstract

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict [...] Read more.

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at

λ

= 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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27 pages, 7589 KiB

Open AccessArticle

Weak Multiple Fault Detection Based on Weighted Morlet Wavelet-Overlapping Group Sparse for Rolling Bearing Fault Diagnosis

by Wan Zhang, Yu Ding, Xiaoan Yan and Minping Jia

Appl. Sci. 2020, 10(6), 2057; https://doi.org/10.3390/app10062057 - 18 Mar 2020

Cited by 9 | Viewed by 2629

Abstract

As one of the important parts of a mechanical transmission system, a rolling bearing often has multiple faults coexisting, and the mutual coupling between multiple faults poses a challenge for accurate diagnosis of rolling bearings. Aiming at the above problems, this paper proposes [...] Read more.

As one of the important parts of a mechanical transmission system, a rolling bearing often has multiple faults coexisting, and the mutual coupling between multiple faults poses a challenge for accurate diagnosis of rolling bearings. Aiming at the above problems, this paper proposes a weighted Morlet wavelet-overlapping group sparse (WOGS) algorithm for the multiple fault diagnosis of rolling bearings. On the basis of the overlapping feature of Morlet wavelet transform coefficients, a WOGS optimization model was initially constructed. Thereafter, the weight coefficients in the model were constructed by analyzing the impulse features of the signal. Thus, majorization-minimization was used to solve the optimization problem. A case study on weak multiple fault diagnosis of rolling bearings was performed to validate the effectiveness of the WOGS algorithm. Quantitative indexes are used to further discuss the extraction accuracies of different algorithms, and the results show that the proposed algorithm exhibits better performance than other algorithms. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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27 pages, 5691 KiB

Open AccessArticle

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

by Xiaoan Yan, Ying Liu, Wan Zhang, Minping Jia and Xianbo Wang

Appl. Sci. 2020, 10(5), 1696; https://doi.org/10.3390/app10051696 - 2 Mar 2020

Cited by 47 | Viewed by 3938

Abstract

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD [...] Read more.

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters (

\hat{α}

, K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ). Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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14 pages, 4756 KiB

Open AccessArticle

Investigation of a Bragg Grating-Based Fabry–Perot Structure Inscribed Using Femtosecond Laser Micromachining in an Adiabatic Fiber Taper

by Aayush Madan, Stephanie Hui Kit Yap, Varghese Paulose, Wonkeun Chang, Perry Ping Shum and Jianzhong Hao

Appl. Sci. 2020, 10(3), 1069; https://doi.org/10.3390/app10031069 - 5 Feb 2020

Cited by 14 | Viewed by 5089

Abstract

This paper presents the fabrication of a fiber Bragg grating (FBG)-based Fabry–Perot (FP) structure (7 mm total length) in an adiabatic fiber taper, investigates its strain and temperature characteristics, and compares the sensing characteristics with a standard polyimide coated FBG sensor. Firstly, a [...] Read more.

This paper presents the fabrication of a fiber Bragg grating (FBG)-based Fabry–Perot (FP) structure (7 mm total length) in an adiabatic fiber taper, investigates its strain and temperature characteristics, and compares the sensing characteristics with a standard polyimide coated FBG sensor. Firstly, a simulation of the said structure is presented, followed by the fabrication of an adiabatic fiber taper having the outer diameter reduced to 70

μ

m (core diameter to 4.7

μ

m). Next, the sensing structure, composed of two identical uniform FBG spaced apart by a small gap, is directly inscribed point-by-point using infrared femtosecond laser (fs-laser) micromachining. Lastly, the strain and temperature behavior for a range up to 3400

μ ε

and 225

^{°}

C, respectively, are investigated for the fabricated sensor and the FBG, and compared. The fabricated sensor attains a higher strain sensitivity (2.32 pm/

μ ε

) than the FBG (0.73 pm/

μ ε

), while both the sensors experience similar sensitivity to temperature (8.85 pm/

^{°}

C). The potential applications of such sensors include continuous health monitoring where precise strain detection is required. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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14 pages, 3902 KiB

Open AccessArticle

Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

by Ying Feng and Jianwen Wu

Appl. Sci. 2020, 10(3), 944; https://doi.org/10.3390/app10030944 - 1 Feb 2020

Cited by 11 | Viewed by 3902

Abstract

As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason [...] Read more.

As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS’s mechanical fault-detection field based on vibration information. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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23 pages, 5636 KiB

Open AccessArticle

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

by Se-Hyeok Lee and Junho Song

Appl. Sci. 2020, 10(3), 850; https://doi.org/10.3390/app10030850 - 25 Jan 2020

Cited by 9 | Viewed by 5043

Abstract

This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system’s states, parameters, input (force), [...] Read more.

This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system’s states, parameters, input (force), or their combinations, the filter proposed in this paper focuses on tracking parameters that may change suddenly using sparse measurements. First, an advanced state-space model of parameter estimation employing a regularization technique is developed to overcome the lack of information in sparse measurements. To avoid inaccurate or biased estimation by conventional filters that use covariance matrices representing time-invariant artificial noises, this paper proposes a dual adaptive filtering, whose slave filter corrects the covariance of the artificial measurement noises in the master filter at every time-step. Since it is generally impossible to tune the proposed dual filter due to sensitivity with respect to parameters selected to describe artificial noises, particle swarm optimization (PSO) is adopted to facilitate optimal performance. Numerical investigations confirm the validity of the proposed method through comparison with other filters and emphasize the need for a thorough tuning process. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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25 pages, 6588 KiB

Open AccessArticle

Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

by Tzu-Kang Lin and Yu-Ching Chen

Appl. Sci. 2020, 10(3), 839; https://doi.org/10.3390/app10030839 - 24 Jan 2020

Cited by 7 | Viewed by 2681

Abstract

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to [...] Read more.

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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12 pages, 4159 KiB

Open AccessArticle

Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process

by Gyungmin Toh, Jaesoo Gwon and Junhong Park

Appl. Sci. 2019, 9(24), 5379; https://doi.org/10.3390/app9245379 - 9 Dec 2019

Cited by 12 | Viewed by 5409

Abstract

This paper presents a novel method to measure clamping force by using the vibration of bolts. The resonance frequency of the bolt increases in line with the clamping force during the tightening process. These characteristics were measured and utilized in the k-means clustering [...] Read more.

This paper presents a novel method to measure clamping force by using the vibration of bolts. The resonance frequency of the bolt increases in line with the clamping force during the tightening process. These characteristics were measured and utilized in the k-means clustering algorithm. Bolt specimens were fastened to the load cell using a nutrunner for verification of the proposed method. The precisely measured clamping force was labeled. The labeled data was used to predict the clamping force from the vibration responses. To use the proposed method in assembly of actual parts, an accelerometer was attached to the nutrunner for vibration measurements. This enabled continuous monitoring of the clamping force without influence on the parts. The estimated clamping force was compared with those from the torque method. When the vibration of a bolt was transmitted through the nutrunner, loss of high-frequency vibration energy occurred. The resonant frequency band vibrations of the bolt were preserved to determine the fastening force. The components in the low frequency band were excluded using a band-pass filter. The clamping force of the bolt used in the vehicle’s lower arm and the link was also evaluated precisely. By using the proposed method, it is possible to continuously monitor variations of the clamping force during the manufacturing process. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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22 pages, 3180 KiB

Open AccessArticle

Frequency Response Analysis of Perforated Shells with Uncertain Materials and Damage

by Harri Hakula and Mikael Laaksonen

Appl. Sci. 2019, 9(24), 5299; https://doi.org/10.3390/app9245299 - 5 Dec 2019

Cited by 8 | Viewed by 2628

Abstract

In this paper, we give an overview of the issues one must consider when designing methods for vibration based health monitoring systems for perforated thin shells especially in relation to frequency response analysis. In particular, we allow either the material parameters or the [...] Read more.

In this paper, we give an overview of the issues one must consider when designing methods for vibration based health monitoring systems for perforated thin shells especially in relation to frequency response analysis. In particular, we allow either the material parameters or the structure or both to be random. The numerical experiments are computed using the standard high order finite element method with stochastic collocation for the cases with random material and Monte Carlo for those with damaged or random structures. The results display a wide range of responses over the experimental configurations. In perforated shell structures, the internal boundary layers can play an important role especially when damage is allowed within the penetration patterns. The computational methodology advocated here can be used to build statistical databases that are necessary for development of probabilistic damage identification methods. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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20 pages, 9352 KiB

Open AccessArticle

Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement

by Zi Huang and Chaoping Zang

Appl. Sci. 2019, 9(23), 4982; https://doi.org/10.3390/app9234982 - 20 Nov 2019

Cited by 11 | Viewed by 2566

Abstract

With the rapid development of a continuously scanning laser Doppler vibrometer (CSLDV) technique, the full-field mode shapes of structures with high accuracy can be obtained. In this paper, a novel damage detection method using modal rotational mode shapes obtained with a uniform rate [...] Read more.

With the rapid development of a continuously scanning laser Doppler vibrometer (CSLDV) technique, the full-field mode shapes of structures with high accuracy can be obtained. In this paper, a novel damage detection method using modal rotational mode shapes obtained with a uniform rate CSLDV measurement is proposed. The modal rotational damage indicators considering the changes of modal rotational mode shapes between the damaged and the undamaged states are established. Because the modal rotational mode shapes are obtained through the derivative of the detailed displacement mode shapes of transitional degree-of-freedoms (DOFs) with respect to the orthogonal directions, they are more sensitive than the normal displacement mode shapes. The uniform rate CSLDV measurement is essentially a uniform straight-line scanning technique and the measured mode shapes can be directly obtained through the demodulation of vibration signals. Besides, taking it for granted that a priori knowledge of the undamaged structure is not known, the undamaged mode shapes can be reconstructed from the measured damaged data using the fitted polynomial functions in which the minimum number of polynomial function coefficients are determined by a fit value threshold. The proposed method is firstly demonstrated by numerical simulation of the crack plate and then a plate structure with three damaged cases is taken as an example for further experimental study. The experimental results indicate the following: (1) The uniform rate CSLDV measurement can obtain the high accuracy modal rotational mode shapes with the advantage of eliminating the contaminated noise in the measurement; (2) the modal rotational damage indicators of the torsional modes are the most sensitive to the crack damage and they can clearly identify single, multiple damages and locations of the plate, and even slight crack damage, respectively. The effectiveness of the method paves the way for practical applications, such as ultra-light or composite structures. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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20 pages, 20854 KiB

Open AccessArticle

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

by Hesheng Tang, Xueyuan Guo, Liyu Xie and Songtao Xue

Appl. Sci. 2019, 9(22), 4959; https://doi.org/10.3390/app9224959 - 18 Nov 2019

Cited by 5 | Viewed by 2760

Abstract

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is [...] Read more.

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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26 pages, 843 KiB

Open AccessArticle

Gradient Descent-Based Optimization Method of a Four-Bar Mechanism Using Fully Cartesian Coordinates

by María T. Orvañanos-Guerrero, Claudia N. Sánchez, Mariano Rivera, Mario Acevedo and Ramiro Velázquez

Appl. Sci. 2019, 9(19), 4115; https://doi.org/10.3390/app9194115 - 1 Oct 2019

Cited by 11 | Viewed by 3064

Abstract

Machine vibrations often occur due to dynamic unbalance inducing wear, fatigue, and noise that limit the potential of many machines. Dynamic balancing is a main concern in mechanism and machine theory as it allows designers to limit the transmission of vibrations to the [...] Read more.

Machine vibrations often occur due to dynamic unbalance inducing wear, fatigue, and noise that limit the potential of many machines. Dynamic balancing is a main concern in mechanism and machine theory as it allows designers to limit the transmission of vibrations to the frames and base of machines. This work introduces a novel method for representing a four-bar mechanism with the use of Fully Cartesian coordinates and a simple definition of the shaking force (ShF) and the shaking moment (ShM) equations. A simplified version of Projected Gradient Descent is used to minimize the ShF and ShM functions with the aim of balancing the system. The multi-objective optimization problem was solved using a linear combination of the objectives. A comprehensive analysis of the partial derivatives, volumes, and relations between area and thickness of the counterweights is used to define whether the allowed optimization boundaries should be changed in case the mechanical conditions of the mechanism permit it. A comparison between Pareto fronts is used to determine the impact that each counterweight has on the mechanism’s balancing. In this way, it is possible to determine which counterweights can be eliminated according to the importance of the static balance (ShF), dynamic balance (ShM), or both. The results of this methodology when using three counterweights reduces the ShF and ShM by 99.70% and 28.69%, respectively when importance is given to the static balancing and by 83.99% and 8.47%, respectively, when importance is focused on dynamic balancing. Even when further reducing the number of counterweights, the ShF and ShM can be decreased satisfactorily. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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19 pages, 5123 KiB

Open AccessArticle

Damping Assessment of Lightweight Timber Floors Under Human Walking Excitations

by Alexander Opazo-Vega, Francisco Muñoz-Valdebenito and Claudio Oyarzo-Vera

Appl. Sci. 2019, 9(18), 3759; https://doi.org/10.3390/app9183759 - 9 Sep 2019

Cited by 14 | Viewed by 15393

Abstract

Vibrations on timber floors are among the most common serviceability problems in social housing projects. The presence of low damping levels on these floors could cause excessive vibrations in a range of frequency and amplitude that generate discomfort in users. This study focuses [...] Read more.

Vibrations on timber floors are among the most common serviceability problems in social housing projects. The presence of low damping levels on these floors could cause excessive vibrations in a range of frequency and amplitude that generate discomfort in users. This study focuses on the influence of the damping ratio in the dynamic serviceability of social housing timber floors due to walking excitations. More than 60 human-walking vibration tests were conducted on both laboratory and in-situ timber floors. The floors were instrumented with accelerometers, and fundamental modal damping ratios were estimated by applying Enhanced Frequency Decomposition Domain (EFDD) and Subspace Stochastic Identification (SSI) methods. The vibration dose value (VDV) was used to estimate the dynamic serviceability of floors. The results indicated that timber floors had an impulsive-type vibration response, with fundamental damping ratios between 1.9% and 14.8%, depending on their constructive characteristics. The in-situ floors had damping ratios between two to three times greater than the laboratory floors due to the presence of non-structural elements. Finally, it was possible to demonstrate that the floors with the highest damping ratios reached lower vibration dose values and, therefore, a better dynamic serviceability performance. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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13 pages, 2572 KiB

Open AccessArticle

A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

by Jiajia Shi, Liu Chu and Eduardo Souza de Cursi

Appl. Sci. 2019, 9(16), 3343; https://doi.org/10.3390/app9163343 - 14 Aug 2019

Cited by 5 | Viewed by 2687

Abstract

The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal [...] Read more.

The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal frequency sensors. In order to reduce the interference of modal frequency sensors for transmission towers, a Kriging surrogate model is proposed in this study. The finite element model of typical transmission towers is created and validated to provide the effective original database for the Kriging surrogate model. The prediction accuracy and convergences of the Kriging surrogate model are measured and confirmed. Besides the merits in computational cost and high-efficiency, the Kriging surrogate model is proven to have a satisfied and robust interference reduction capacity. Therefore, the Kriging surrogate model is feasible and competitive for interference filtration in the settlement surveillance sensors of steel transmission towers. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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13 pages, 2002 KiB

Open AccessArticle

Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique

by Hitoshi Tsunashima

Appl. Sci. 2019, 9(13), 2734; https://doi.org/10.3390/app9132734 - 5 Jul 2019

Cited by 74 | Viewed by 9261

Abstract

A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track [...] Read more.

A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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12 pages, 942 KiB

Open AccessArticle

Simple Degree-of-Freedom Modeling of the Random Fluctuation Arising in Human–Bicycle Balance

by Katsutoshi Yoshida, Keishi Sato and Yoshikazu Yamanaka

Appl. Sci. 2019, 9(10), 2154; https://doi.org/10.3390/app9102154 - 27 May 2019

Cited by 5 | Viewed by 3198

Abstract

In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance [...] Read more.

In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance motions and construct their PDFs. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization; in particular, we minimize the Kolmogorov–Smirnov distance between the human PDFs from the participants and the PDFs simulated by our model. The resulting PDF fitnesses were over

98.7 %

for all participants, indicating that our simulated PDFs were in close agreement with human PDFs. Furthermore, the Kolmogorov–Smirnov statistical hypothesis testing was applied to the resulting human–bicycle fluctuation model, showing that the measured time responses were much better supported by our model than the Gaussian distribution. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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26 pages, 8745 KiB

Open AccessArticle

Reliability of Field Experiments, Analytical Methods and Pedestrian’s Perception Scales for the Vibration Serviceability Assessment of an In-Service Glass Walkway

by Chiara Bedon and Marco Fasan

Appl. Sci. 2019, 9(9), 1936; https://doi.org/10.3390/app9091936 - 11 May 2019

Cited by 35 | Viewed by 4759 | Correction

Abstract

The vibration performance of pedestrian structures attracts the attention of several studies, especially with respect to unfavorable operational conditions or possible damage scenarios. Given a pedestrian system, specific vibration comfort levels must be satisfied in addition to basic safety requirements, depending on the [...] Read more.

The vibration performance of pedestrian structures attracts the attention of several studies, especially with respect to unfavorable operational conditions or possible damage scenarios. Given a pedestrian system, specific vibration comfort levels must be satisfied in addition to basic safety requirements, depending on the class of use, the structural typology and the materials. To this aim, guideline documents of the literature offer simplified single-degree-of-freedom (SDOF) approaches to estimate the maximum expected vibrations and to verify the required comfort limits. Most of these documents, however, are specifically calibrated for specific scenarios/structural typologies. Dedicated methods of design and analysis, in this regard, may be required for structural glass pedestrian systems, due to their intrinsic features (small thickness-to-size ratios, high flexibility, type and number of supports, live-to-dead load ratios, use of materials that are susceptible to mechanical degradation with time/temperature/humidity, etc.). Careful consideration could be then needed not only at the design stage, but also during the service life of a given glass walkway. In this paper, the dynamic performance of an in-service glass walkway is taken into account and explored via field vibration experiments. A set of walking configurations of technical interest is considered, involving 20 volunteers and several movement features. The vibration comfort of the structure is then assessed based on experimental estimates and existing guideline documents. The intrinsic uncertainties and limits of simplified approaches of literature are discussed, with respect to the performance of the examined glass walkway. In conclusion, the test predictions are also used to derive “perception index” data and scales that could support a reliable vibration comfort assessment of in-service pedestrian glass structures. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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Review

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24 pages, 4503 KiB

Open AccessReview

Review of Vibration-Based Structural Health Monitoring Using Deep Learning

by Gyungmin Toh and Junhong Park

Appl. Sci. 2020, 10(5), 1680; https://doi.org/10.3390/app10051680 - 2 Mar 2020

Cited by 136 | Viewed by 14823

Abstract

With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the [...] Read more.

With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis. Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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Other

Jump to: Editorial, Research, Review

1 pages, 163 KiB

Open AccessCorrection

Correction: Bedon, C.; Fasan, M. Reliability of Field Experiments, Analytical Methods and Pedestrian’s Perception Scales for the Vibration Serviceability Assessment of an In-Service Glass Walkway. Appl. Sci. 2019, 9, 1936

by Chiara Bedon and Marco Fasan

Appl. Sci. 2020, 10(3), 1032; https://doi.org/10.3390/app10031032 - 4 Feb 2020

Cited by 1 | Viewed by 1665

Abstract

We, the authors, wish to make the following corrections to our paper [...] Full article

(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)

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