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 >

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 >

S.C. Forth¹, A. Staroselsky²

CMES-Computer Modeling in Engineering & Sciences, Vol.9, No.3, pp. 287-298, 2005, DOI:10.3970/cmes.2005.009.287

Abstract A new hybrid surface-integral-finite-element numerical scheme has been developed to model a three-dimensional crack propagating through a thin, multi-layered coating. The finite element method was used to model the physical state of the coating, and the surface integral method was used to model the fatigue crack growth. The two formulations are coupled through the need to satisfy boundary conditions on the crack and external surface. The coupling is sufficiently weak that the surface integral mesh of the crack surface and the finite element mesh of the uncracked volume can be set up independently. Thus, when modeling crack growth, the finite… More >

A. Joshuva¹, V. Sugumaran²

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287

Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on… More >

Mengyue He¹, Zhifang Zhang^1,*, Karthik Ram Ramakrishnan²

Structural Durability & Health Monitoring, Vol.12, No.4, pp. 213-256, 2018, DOI:10.32604/sdhm.2018.05122

Abstract Fibre reinforced polymer (FRP) composite laminates are now commonly used in many structural applications, especially in the aerospace industry, where margins of safety are kept low in order to minimise weight. Timely detection and assessment of damage (in particular delaminations) in composite laminates are therefore critical, as they can cause loss of structural integrity affecting the safe operation of the composite structures. The current trend is towards implementation of structural health monitoring (SHM) systems which can monitor the structures in situ without down time. In this paper, first, the current available SHM techniques for delamination detection in FRP composites are… More >

Kong Fah Tee ^1,*

Structural Durability & Health Monitoring, Vol.12, No.3, pp. 129-147, 2018, DOI: 10.3970/sdhm.2018.04316

Abstract Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace, mechanical and civil structures. The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection. Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods. In the literature on the structural damage detection, many time series-based methods have been proposed. When a considered time series model approximates the vibration response of a structure and model coefficients… More >

Biao Zhou^1,2,3, Xiong yao Xie^1,2, Yeong Bin Yang⁴, Jing Cai Jiang³

CMES-Computer Modeling in Engineering & Sciences, Vol.86, No.4, pp. 321-348, 2012, DOI:10.3970/cmes.2012.086.321

Abstract The vibration-based SHM (Structure Health Monitoring) system has been successfully used in bridge and other surface civil infrastructure. However, its application in operation tunnels remains a big challenge. The reasons are discussed in this paper by comparing the vibration characteristics of the free tunnel structure and tunnel-soil coupled system. It is revealed that all the correlation characteristics of the free tunnel FRFs (Frequency Response Function spectrum) will vanish and be replaced by a coupled resonance frequency when the tunnel is surrounded by soil. The above statement is validated by field measurements. Moreover, the origin of this phenomenon is investigated by… More >