This paper describes a rail inspection prototype based on noncontact probing and ultrasonic guided waves coupled with a robust signal processing algorithm. The algorithm consists of discrete wavelet transform, feature extraction, and outlier analysis aimed at providing automatic damage detection and classification. The system uses laser generated guided waves to detect surface-breaking cracks and internal defects located in the rail head. Ultrasonic signals were detected by using three pairs of air-coupled transducers. Time waveforms were processed with the Discrete Wavelet Transform to denoise the signals and to generate a set of relevant damage sensitive features used to construct a uni or multidimensional damage index. The damage index was fed to an unsupervised learning algorithm based on outlier analysis aimed at detecting anomalous conditions of the rail head. The population of data for the outlier analysis was created by adding digital random noise to the ultrasonic measurements. A total of 20 samples per acquisition were thus obtained for the baseline constituting the undamaged condition and for each of the damage conditions.
wavelet toolbox matlab 2010 a crack
Parallel with significant growth in industry, especially mysteries related to energy engineering, condition monitoring of rotating systems have been experiencing a noticeable increase. One of the prevalent faults in these systems is fatigue crack, so finding reliable procedures in identification of cracks in rotating shafts has become a pressing problem among engineers during recent decades. While a vast majority of cracked rotors can operate for a specific period of time, to prevent catastrophic failures, crack detection and measuring its characteristics (i.e. size and its location) seem to be essential. In the present essay, a hybrid procedure, consisting of Deep Learning and Discrete Wavelet transform (DWT), is applied in detection of a breathing transverse crack and its depth in a rotor-bearing-disk system. DWT with Daubechies 32(db32) as wavelet mother function is applied in signal noise reduction until level 6, also its Relative Wavelet Energy (RWE) and Wavelet entropy (WE) are extracted. A characteristic vector that is a combination of RWE and WE is considered as input to a multi-layer Artificial Neural Network (ANN). In this supervised learning classifier, a multi-layer Perceptron neural network is used; in addition, Rectified Linear Unit (ReLU) function is exerted as activation function in both hidden and output layers. By comparing the results, it can be seen that the applied procedure has strong capacity in identification of crack and its size in the rotor system.
In [12] two approaches were used for crack detection in rotating machinery, model-based and signal-based approaches, were compared. Strength and weak points were discussed and compared for the two approaches using two representative applicable methods, in order to achieve a comparative overview of these two available techniques. Söffker et al. employed Proportional-Integral-Observer approach (i.e. is a novel model-based procedure) in demonstrating model-based capacities and restrictions. As a result, they presented a modern signal-technique which is a combination of support vector machine and wavelet transform. An intensive review on almost all applied procedures in the field of crack detection carried out by Sabnavis [13].
From the graphs it can be seen that near to the second 1 the amplitude of vibration signal has a rapid growth due to crack, and this jump increases by increasing crack depth. This change is denoted in the last graph by a red circle. In current work, concept of relative wavelet energy and wavelet entropy are used in forming feature vector to classifying shafts. Fig. 6 demonstrates wavelet coefficients (i.e. detail and approximation) of cracked, in class 4, and intact rotors, belonging to class 1, until level 6 by means of db32 as wavelet mother function.
In this research, a hybrid procedure consisting of discrete wavelet transform and deep learning procedures are employed in classifying cracked shafts in a rotating system with various crack depths. At the initial step of signal processing, collected signals are noise reduced by the help of discrete wavelet method to level 6. In the following, relative wavelet energy and wavelet entropy of vibration signals are calculated. Feature vectors are extracted based on RWE and WE. Then, these features are used in classifying different classes of shaft (i.e. healthy and cracked rotors in three depths). To classify, a multi-hidden layer Perceptron algorithm with Rectified Linear Unit (ReLU) function is exerted as activation function is introduced. By using ReLU, the Perceptron algorithm avoided overfitting, so the results that are shown in Fig. 8 state that this hybrid method has accuracy above 99.5 percent. This threshold of accuracy approves the fact that the introduced manner in classifying cracked rotors in consideration with crack size has reasonable success. 2ff7e9595c
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