Analysis of the vibration characteristics of an experimental mechanical system using neural networks

Erkaya, Selçuk

doi:10.1177/1077546311429059

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Also, these networks are validated and tested with 52 and 48 data, respectively, in responses to input which have not been used in the training step. The percentage of these data sets is nearly consistence with literature [ 5 , 21 , 23 ]. After the training and testing stages of networks are completed, the weights are saved and used for predicting and estimating the environmental and tram noises for given an input matrix.…”

Section: Resultsmentioning

confidence: 66%

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Modelling and evaluation of light railway system’s noise using neural predictors

Erkaya

Geymen

Bostancı

2015

J Environ Health Sci Engineer

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BackgroundNoise is defined as a sound or series of sounds that are considered to be invasive, irritating, objectionable and disruptive to the quality of daily life. Noise is one of the environmental pollutants, and in cities it is usually originated from road traffic, railway traffic, airports, industry etc. The tram is generally considered as environmentally friendly, namely non-polluting and silent. However complaints from residents living along the tramway lines prove that it may sometimes cause annoyance. In this study, a Global Pointing System (GPS) receiver for determining the sampling locations and a frequency based noise measurement system for collecting the noise data are used to analyse the noise level in the city centre. Both environmental (background) and tram noises are measured.ResultsThree types of neural networks are used to predict the noises of the tram and environment. The results of three approaches indicate that the proposed neural network with Radial Basis Function (RBF) has superior performance to predict the noises of the tram and environment.ConclusionsFor making a decision about transportation planning, this network model can help urban planners for evaluating and/or isolating the tram noise in terms of human health.

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Section: Resultsmentioning

confidence: 66%

“…After processing in hidden layer, the network model has got one weight value connected to the output layer. A linear squares regression algorithm is used to train the weight of output layer [ 21 ]. Mean square of the error (MSE) is considered as a performance measuring index of neural model.…”

Section: Methodsmentioning

confidence: 99%

Modelling and evaluation of light railway system’s noise using neural predictors

Erkaya

Geymen

Bostancı

2015

J Environ Health Sci Engineer

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“…The sigmoid function is also used in four hidden layers with ten neurons. In this study, the Levenberg-Marquardt algorithm is adopted for the learning process due to its fast convergence properties [55]. The performance index used in NARX model training is the mean squared error (MSE), which is one of the typical performance functions.…”

Section: Y Y Y Y U U Umentioning

confidence: 99%

Effects of Joint Clearance on the Motion Accuracy of Robotic Manipulators

2018

SV-JME

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Accuracy is one of the key features that improves the quality of robotic manipulators for many industrial and medical applications. Even with an accurate design and manufacturing process, clearance problems in joints cannot be eliminated in articulated systems. This leads to a loss of accuracy in robotic manipulation. In this study, the effects of joint clearance in a robotic system are investigated. The need for the trajectory of end effector is considered to analyse the motion sensitivity. The kinematics and dynamics of a six-DOF robot with joint clearance are evaluated relative to a robot without joint clearance. Different scenarios for clearance values and working periods are performed to fulfil the required motion task. A neural model is also used to predict the trajectory deviations during the working process. The results show that clearance has a significant role in the motion sensitivity of robotic manipulations. Trajectory errors can also be determined by using a dynamic neural predictor with suitable input variables.

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“…Over the past three decades tremendous progress has been made in the area of vibration signal processing with the aim of achieving better accuracy in machine fault diagnostics. Techniques such as exact wavelet analysis (EWA) (Tse et al, 2004), Hilbert-Huang transform (HHT) (Peng et al, 2005), singular value decomposition (SVD) (Xi et al, 2000;Yang and Tse, 2003), and neural networks and learning machines (Erkaya, 2011) have been thoroughly studied. Many of these techniques involve analyzing the vibration signal in time, frequency, and joint time-frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation for vibration-based diagnostics of rotorcraft bearings

Haile

Dykas

2016

Journal of Vibration and Control

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The vibration signals from sensors monitoring the activity of individual bearings in a power train unit may be linear instantaneous mixtures of vibrations generated by various dynamic components. Generally, an exact physical model describing the mixing process and the contribution of each dynamic component to the received sensor signal is not available. Vibration source signals from defective bearings often overlap in time and frequency, and, as such, the direct use of time- and frequency-domain methods may result in erroneous diagnostic information. This paper implements blind source separation (BSS) to demix sensor signals into correctly identifiable vibration source signals without the need of the vibration path property and sensor layout. Experimental vibration data from spalled, corroded, and healthy rotorcraft bearings are used with five representative BSS algorithms. The separation accuracy of these algorithms is then compared using various performance metrics. Results show that despite the inherent statistical dependence and near Gaussianity, it is possible to isolate vibration sources from mixed sensor signals using second- and higher-order statistics of the signals. The paper also identifies the limitations of the BSS technique and provides a remedy and recommendation for its implementation in rotorcraft bearing fault detection.

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Analysis of the vibration characteristics of an experimental mechanical system using neural networks

Cited by 7 publications

References 11 publications

Modelling and evaluation of light railway system’s noise using neural predictors

Modelling and evaluation of light railway system’s noise using neural predictors

Effects of Joint Clearance on the Motion Accuracy of Robotic Manipulators

Blind source separation for vibration-based diagnostics of rotorcraft bearings

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