J.K. Mohanty¹, A. Adarsh², P.R. Dash¹, K. Parida¹, P.K. Pradhan^1,*

Sound & Vibration, Vol.53, No.5, pp. 223-235, 2019, DOI:10.32604/sv.2019.07737

Abstract Condition monitoring is implementation of the advanced diagnostic techniques to reduce downtime and to increase the efficiency and reliability. The research is for determining the usage of advanced techniques like Vibration analysis, Oil analysis and Thermography to diagnose ensuing problems of the Plant and Machinery at an early stage and plan to take corrective and preventive actions to eliminate the forthcoming breakdown and enhancing the reliability of the system. Nowadays, the most of the industries have adopted the condition monitoring techniques as a part of support system to the basic maintenance strategies. Major condition monitoring… More >

Chuiju You¹, Dongjie Zhu^2,*, Yundong Sun², Anshan Ye³, Gangshan Wu⁴, Ning Cao¹, Jinming Qiu¹, Helen Min Zhou⁵

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1271-1283, 2019, DOI:10.32604/cmc.2019.06133

Abstract With the rapid development of social network, public opinion monitoring based on social networks is becoming more and more important. Many platforms have achieved some success in public opinion monitoring. However, these platforms cannot perform well in scalability, fault tolerance, and real-time performance. In this paper, we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch (SNES). Firstly, SNES integrates the module of distributed crawler cluster, which provides real-time social media data access. Secondly, SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time. Finally, we design subscription More >

Selma B. Jaconis^1,*, Augusto T. Morita², Paulo L. A. Coutinho³, Suzana Borschiver¹

Journal of Renewable Materials, Vol.7, No.11, pp. 1221-1230, 2019, DOI:10.32604/jrm.2019.00025 - 14 July 2021

Abstract In recent years environmental and sustainability concerns have impacted the global chemical industry and instituted a rush to produce products from renewable raw materials. This dynamic, complex and turbulent organizational scenario, around themes touching on the issue of sustainable development model, was created involving a large number of different actors: chemical/petrochemical industries, agroindustry companies, oil/gas companies, brand owners and end users, biotechnology startups, governments, universities and society. This paper proposed the application of a structured and dynamic method of technological prediction for biopolymers in three levels: systematic monitoring process, relational database and the “alive” Technology More >

Longqing Zhang¹, Liping Bai^1,*, Xinwei Zhang², Yanghong Zhang², Lei Yang¹, Xiangdu Yan²

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 817-828, 2019, DOI:10.32604/cmc.2019.06062

Abstract The drawbacks of the traditional multi-hop structure of wireless sensor networks (WSNS) are susceptible to reflection, scattering and diffraction, serious attenuation of signal intensity and phase deviation due to various obstacles in cultivated land environment and mountains. In this paper, the wireless signal intensity is measured and curve fitting is done respectively in both open environment and obstacle-environment with both sending and receiving height of 0 m, 1 m, and 2 m. It is found out that packet loss probability is too high when an obstacle exists. In such a case, unmanned aerial vehicle (UAV) 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… 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… 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 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… 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… 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 More >