Chang Kook Oh¹, Hoon Sohn¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.8, No.3, pp. 91-100, 2008, DOI:10.3970/icces.2008.008.091

Abstract Structural Health Monitoring (SHM) is concerned with identifying damage based on measurements obtained from structures being monitored. For the civil structures exposed to time-varying environmental and operational conditions, it is inevitable that environmental and operational variability produces an adverse effect on the dynamic behaviors of the structures. Since the signals are measured under the influence of these varying conditions, normalizing the data to distinguish the effects of damage from those caused by the environmental and operational variations is important in order to achieve successful structural health monitoring goals. In this paper, kernel principal component analysis (kernel PCA) using unsupervised support… More >

C. A. Featherston¹, M. Eaton¹, R. Pullin¹, K. M. Holford¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.10, No.1, pp. 29-36, 2009, DOI:10.3970/icces.2009.010.029

Abstract This article has no abstract. More >

Jeffrey T. Fong^1,*, Pedro V. Marcal², Robert Rainsberger³, N. A. Heckert¹, J. J. Filliben¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.2, pp. 45-45, 2019, DOI:10.32604/icces.2019.05432

Abstract When a small crack is detected in a pressure vessel or piping, we can estimate the fatigue life of the vessel or piping by applying the classical law of fracture mechanics for crack growth if we are certain that the crack growth exponent is correct and the crack geometry is a simple plane. Unfortunately, for an ageing vessel or piping, the degradation will change the crack growth exponent, and the crack will advance not in a simple planar fashion. To validate the crack growth exponent for an ageing vessel or piping with a crack of an irregular shape, we design… More >

Kundur Shantisagar, R. Jegadeeshwaran^*, G. Sakthivel, T. M. Alamelu Manghai

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 303-316, 2019, DOI:10.32604/sdhm.2019.00355

Abstract The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired… More >

Jun Wu^1,*, Kui Hu¹, Yiwei Cheng², Ji Wang¹, Chao Deng^2,*, Yuanhan Wang³

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN),… More >

Guang Chen^1,*, Keqin Ding¹, Qibo Feng², Xinran Yin¹, Fangxiong Tang¹

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 289-301, 2019, DOI:10.32604/sdhm.2019.05144

Abstract The grating ends bonding fiber Bragg grating (FBG) sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures. However, owing to the shear deformation of the adhesive layer of FBG, the strain measured by FBG is often different from the strain of actual matrix, which causes strain measurement errors. This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG. To fulfill this objective, a strain transfer equation of the grating ends bonding FBG is derived, and a theoretical model of the average strain… More >

Rui Lu¹, Zhanjun Wu¹, Qi Zhou¹, Hao Xu^1,*

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 267-287, 2019, DOI:10.32604/sdhm.2019.06323

Abstract Structural health monitoring (SHM) is a research focus involving a large category of techniques performing in-situ identification of structural damage, stress, external loads, vibration signatures, etc. Among various SHM techniques, those able to monitoring structural deformed shapes are considered as an important category. A novel method of deformed shape reconstruction for thin-walled beam structures was recently proposed by Xu et al. [1], which is capable of decoupling complex beam deformations subject to the combination of different loading cases, including tension/compression, bending and warping torsion, and also able to reconstruct the full-field displacement distributions. However, this method was demonstrated only under… More >

Alex Kost¹, Wael A. Altabey^2,3,4, Mohammad Noori^1,2,*, Taher Awad⁴

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 247-266, 2019, DOI:10.32604/sdhm.2019.07025

Abstract A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various parameters are compared. Finally, future work and improvements are discussed. More >

Shuangsheng Zhang^1,5, Hanhu Liu¹, Jing Qiang^2,*, Hongze Gao^3,*, Diego Galar⁴, Jing Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825

Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X₀ , Y₀) and release time (T₀), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater… More >

Lihui Wang¹, Feng Qi¹, Xin Shen^2,3,*, Jinliang Huang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 331-348, 2019, DOI:10.32604/cmes.2019.04268

Abstract Multiple cropping index (MCI) is a very important indicator in crop production and agricultural intensification, which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land. The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS (Moderate-Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index, and the method of no additional authentication data is independent and reliable.… More >