Applied Physics

An improved stochastic averaging process on a monostable piezoelectric vibrational energy harvester model excited by colored noise
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Research Article
An improved stochastic averaging process on a monostable piezoelectric vibrational energy harvester model excited by colored noise
By Bo Li, Yusen Li, Gen Ge
A monostable piezoelectric vibration energy harvester (VEH) model subject to Gaussian colored noise is studied in this paper. With the help of energy balance method, a concise expression of the transient frequency which is determined by transient amplitude is used in the stochastic averaging process. Then an Itô stochastic differential equation is obtained. The new expression of frequency can lead to pretty good probability density function (PDF) of the displacement, PDF of output electric voltage of the VEH model, even if the nonlinear stiffness coefficient is very large. The influence of the nonlinear stiffness coefficient on the PDFs and on the output electric voltage is detailed and discussed. It is found that the larger nonlinear stiffness coefficient is, the smaller motion range and smaller mean square value of electric voltage it will result in. Furthermore, the larger time delay coefficient of the colored noise is, the larger mean square value of electric voltage it will lead to. Numerical simulations verified the accuracy of this method.
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Applied Physics
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A conversion guide: solar irradiance and lux illuminance
By Peter R. Michael, Danvers E. Johnston, Wilfrido Moreno
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Applied Physics
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A portable breast cancer detection system based on smartphone with infrared camera
By Jian Ma, Pengchao Shang, Chen Lu, Safa Meraghni, Khaled Benaggoune, Juan Zuluaga, Noureddine Zerhouni, Christine Devalland, Zeina Al Masry
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Biomechanics
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Experimental kinematic analysis of an intermittent motion planetary mechanism with elliptical gears
By Alexander Prikhodko
September 30, 2020
Applied Physics
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Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant
By I. P. Okokpujie, L. K. Tartibu
December 13, 2021
Applied Physics

Journal of Vibroengineering

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review
Research Article
Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review
There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised.
November 26, 2021
Applied Mathematics
A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis
Research Article
A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis
The extraction of early fault features from time-series data is very crucial for convolutional neural networks (CNNs) in bearing fault diagnosis. To address this problem, a CNN framework based on identity mapping and Adam optimizer is presented for learning temporal dependencies and extracting fault features. The introduction of four identity mappings allows the deep layers to directly learn the data from the shallow layers, which alleviates the gradient disappearance problem caused by the increase of network depth. A new Adam optimizer with power-exponential learning rate is proposed to control the iteration direction and step size of CNN method, which solves the problems of local minima, overshoot or oscillation caused by the fixed values of the learning rates during the updating of network parameters. Compared to existed methods, the identification accuracy of the proposed method outperformed that of other methods for bearing fault diagnosis.
June 30, 2022
Applied Mathematics
A review on wind turbines gearbox fault diagnosis methods
Research Article
A review on wind turbines gearbox fault diagnosis methods
As an renewable and clean energy of the world, wind energy has gained more and more attention and its fault diagnosis becomes more and more important. The gearbox, as the kernel component of the wind turbine system, it’s robust conditions have a great influence on the whole wind turbines system. Wind turbine gearbox has complex structure, which is usually composed of solar planetary gearbox and cylindrical gearbox. In the process of operation, various kinds of faults easily occur, resulting in serious losses. Once the wind turbine gearbox is not functioning as smoothly as it could be, it may result in large economic losses for the company and owner. At the same time, the failure rate of wind turbine gearbox has always been high because of complicated mechanic structure and special motion. Therefore, the tasks of reducing the downtime and increasing the productivity of wind turbine gearbox are urgent. This paper reviewed some research results of faults diagnosis on wind turbines gearbox, such as time-frequency analysis method, vibration based methods, nondestructive testing methods, etc. Meanwhile, this paper finds out some key problems and the channel of the resolution of the issue in order to supply some information for the further research of wind turbines gearbox.
January 27, 2021
Applied Mathematics
Applying deep learning and wavelet transform for predicting the vibration behavior in variable thickness skew composite plates with intermediate elastic support
Research Article
Applying deep learning and wavelet transform for predicting the vibration behavior in variable thickness skew composite plates with intermediate elastic support
In this paper, the vibration behavior features are extracted from the combination between Wavelet Transform (WT), and Finite Strip Transition Matrix (FSTM) of skew composite plates (SCPs), with variable thickness, and intermediate elastic support. Although, the results of this technique and based on the previous work done by the authors, that show the method can reflect the vibration behavior of the composite plates. Due to the method's difficulty in terms of, a lot of calculations with a large number of iterations these results may not be good choices for quick and accurate vibration behavior extracting. Thus, the new deep neural network (NN) is designed to learn and test these results carrying out by extracting vibration behavior features that reflect the important and essential information about the mode shapes in SCP. The results give high indications about the proposed technique of deep learning is a promising method, particularly when the type structures are complicated and the ambient environment is variable.
February 6, 2021
Vibration Engineering
Journal of Vibroengineering

Comprehensive platform for advancements in the field of vibration engineering

Impact Factor
1.0
CiteScore
1.7
APC
1050 EUR

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