Open Access

ARTICLE

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 >

View

4372

Download

3163

Like

2

Cited by

5

Open Access

ARTICLE

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 >

View

1950

Download

1541

Like

2

Cited by

1

Open Access

ARTICLE

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 >

View

2105

Download

1537

Like

2

Cited by

2

Open Access

ARTICLE

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 >

View

2254

Download

1632

Like

2

Cited by

3

Open Access

ARTICLE

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 >

View

2553

Download

1769

Like

2

Cited by

1