Rahul Kumar Sharma^*1, V. Sugumaran¹, Hemantha Kumar², Amarnath M³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost… More >

Xixiang Chen¹, Yu Chen^{2, *}, Kang He², Fernando Palacios Galarza³

Structural Durability & Health Monitoring, Vol.12, No.4, pp. 299-322, 2018, DOI: doi:10.32604/sdhm.2018.04625

Abstract In order to studythe web-crippling behavior of aluminum hollow section subjected toconcentrated load, sixteen aluminum hollow tubes withdifferent loading conditions, bearing lengthandweb slenderness ratioswere tested. This paperalso discusseda method to improve the web crippling strength of the aluminum hollow sections byinfilling the mortar as composite section, and four aluminum composite sections were tested. The literature has reported lots of web crippling tests, butthere isfew reportson web crippling behavior of aluminum composite sections. Interior-Ground(IG) and End-Ground(EG)loading conditions were adopted, with the specimens placed on the ground to simulate the load of floor joists. Specimens were also placed on a bearing plate… 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 >

Jieping Liu¹, Lingxin Zhang^1,*, Haohao Zhang², Tao Liu¹

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 123-142, 2019, DOI:10.32604/sdhm.2019.04486

Abstract As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed that, with different designs at… More >

Nan Guo^1,*, Wenbo Wang¹, Hongliang Zuo¹

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 143-179, 2019, DOI:10.32604/sdhm.2019.04640

Abstract Applying pre-stress in glulam beam can reduce its deformation and make full use of the compressive strength of wood. However, when the glulam with low strength and the pre-stressed steel with high strength form combined members, materials of high strength can’t be fully utilized. Therefore, this study puts forward the idea of regulating and controlling string beam of pre-stressed glulam. By regulating and controlling the pre-stress, a part of the load borne by the wood is allocated to the pre-stressed tendon, which is equivalent to completing a redistribution of internal force, thus realizing the repeated utilization of the wood strength… 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 >