Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Liang, Bo; Iwnicki, Simon; Ball, Andrew; Young, Andrew E

doi:10.1016/j.ymssp.2014.06.012

Cited by 56 publications

(27 citation statements)

References 23 publications

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“…In the last decade, different object recognition methods have been used in the field of rail defect detection. In [4], [15]- [17], some signal processing techniques such as noise reduction and wavelet transforms have been used for estimating irregularities and detecting defects in railway tracks. Unlike signal processing, the use of image processing techniques and image data analysis is a very recent approach for detection of rail defects.…”

Section: Related Workmentioning

confidence: 99%

Deep convolutional neural networks for detection of rail surface defects

Faghih-Roohi

Hajizadeh

Núñez

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

265

100

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In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detection of rail defects can help to save time and costs, and to ensure rail transportation safety. However, one major challenge is that the extraction of suitable features for detection of rail surface defects is a non-trivial and difficult task. Therefore, we propose to use convolutional neural networks as a viable technique for feature learning. Deep convolutional neural networks have recently been applied to a number of similar domains with success. We compare the results of different network architectures characterized by different sizes and activation functions. In this way, we explore the efficiency of the proposed deep convolutional neural network for detection and classification. The experimental results are promising and demonstrate the capability of the proposed approach.Accepted Author Manuscript. Link to published article (IEEE): http://dx.

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Section: Related Workmentioning

confidence: 99%

Deep convolutional neural networks for detection of rail surface defects

Faghih-Roohi

Hajizadeh

Núñez

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

265

100

View full text Add to dashboard Cite

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“…Because of the tiny vibration of the velocity step response of the drive side (shown in Figure 6(a)), time-frequency analysis (TFA) is used for obtaining the resonance frequency of the system by revealing the energy size of drive-side velocity in each time period. One of the most popular methods of TFA is the short-time Fourier transform (STFT) [13]. When the STFT is used, it is considered that a certain width of window sliding in the nonstationary signal is assumed to be piecewise stationary.…”

Section: Identification Of the Resonance Frequencymentioning

confidence: 99%

“…Velocity step response of the drive side as the test signal is processed by using Hanning-window in MATLAB. According to the experimental data, for example, as in Figure 6(a), the sampling frequency is set as 100 Hz (equivalent to Δ = 10 ms in (13)) and sampling as 30 s. In this case, tiny vibration is shown in the first 500 data points out of 3000 data points ( = 0, 1, 2, . .…”

Section: Identification Of the Resonance Frequencymentioning

confidence: 99%

IP Controller Design for Uncertain Two‐Mass Torsional System Using Time‐Frequency Analysis

Cui

Chu

2018

Shock and Vibration

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With the development of industrial production, drive systems are demanded for larger inertias of motors and load machines, whereas shafts should be lightweight. In this situation, it will excite mechanical vibrations in load side, which is harmful for industrial production when the motor works. Because of the complexity of the flexible shaft, it is often difficult to calculate stiffness coefficient of the flexible shaft. Furthermore, only the velocity of driving side could be measured, whereas the driving torque, the load torque, and the velocity of load side are immeasurable. Therefore, it is inconvenient to design the controller for the uncertain system. In this paper, a low-order IP controller is designed for an uncertain two-mass torsional system based on polynomial method and time-frequency analysis (TFA). IP controller parameters are calculated by inertias of driving side and load side as well as the resonant frequency based on polynomial method. Therein, the resonant frequency is identified using the time-frequency analysis (TFA) of the velocity step response of the driving side under the open-loop system state, which can not only avoid harmful persistent startstop excitation signal of the traditional method, but also obtain high recognition accuracy under the condition of weak vibration signal submerged in noise. The effectiveness of the designed IP controller is verified by groups of experiments. Experimental results show that good performance for vibration suppression is obtained for uncertain two-mass torsional system in a medium-low shaft stiffness condition.

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“…The monitoring systems for inservice and onboard inspections usually adopted ultrasonic technique [26], acoustic technique [27], vibration technique [2,[28][29][30][31] and magnetic technique [32] to detect flange contact [26], surface defects of wheel-set [26][27][28][29][30], and derailment coefficient [32], respectively. Liang et al analyzed the vibration signals and acoustic signals stimulated by the wheel flat and rail surface defects at different speeds levels [29].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Fault Diagnosis of Wheel Flat Using Empirical Mode Decomposition-Hilbert Envelope Spectrum

Jiang

Lin

2018

Mathematical Problems in Engineering

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We establish the Injury Model of Wheel Flats with 10 degrees of freedom and calculate the dynamic responses of the railway vehicle system, which include different vehicle speeds and different length flats. The Hilbert envelope spectrum method based on Empirical Mode Decomposition (EMD) is proposed according to the nonstationary characteristics of axle box acceleration (ABA) signal. The vibration characteristics of the ABA are studied thoroughly. And then the effects concerning speed and flat length on the diagnosis results are analyzed. The simulation results show the amplitude corresponding to the frequency component of wheel flats raise with the increasing of the wheel flat length when the single or double wheel flats impact the track at the same vehicle speed. In other words, the longer the wheel flat is, the greater the magnitude of the decomposition result is. In the same vehicle speed, the amplitude corresponding to the frequency component of wheel flat is minimum when the two flats’ phase difference is 180°. With the same flat length (single or double wheel flats), the amplitude corresponding to the frequency components of wheel flats decreases with the increasing of the speed. This method could accurately and effectively identify the frequency of wheel flats.

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Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Cited by 56 publications

References 23 publications

Deep convolutional neural networks for detection of rail surface defects

Deep convolutional neural networks for detection of rail surface defects

IP Controller Design for Uncertain Two‐Mass Torsional System Using Time‐Frequency Analysis

Fault Diagnosis of Wheel Flat Using Empirical Mode Decomposition-Hilbert Envelope Spectrum

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