Switch rails are indispensable components of high speed railway systems, which have stringent nondestructive testing requirements owing to the severe operating conditions. In this article, an ultrasonic guided wave method is proposed for defect detection and localization using independent component analysis (ICA). The temperature effect is included in the data matrix by a random selection of the signals measured at different temperatures. A damage index named the average standard Euclidian distance (ASED) is used to evaluate the deviations of the test signals from the baseline signals in the feature space consisting of the independent components for the defect detection. Once the defect existence is found, defect localization is conducted by another ICA-based decomposition of a new data matrix with additional test signals for the same defect. Independent components whose coefficient vectors show a high correlation with the standard step change vector are chosen to construct the ICA-based residual signal. Then the time instance and location of the defect is determined by observing the first very high peak occurring in the residual signals. A detectability index for defect location (DIDL) is proposed. Experimental validations are performed for the defects on the foot and web of a switch rail. The results of the ASED curves clearly indicate the existence of artificial defects, and the ICA-based residual signals show the location of the defects. The proposed method is found to be superior to conventional methods such as simple baseline subtraction and optimal baseline subtraction regarding the DIDL.
Switch rails are weak but essential components of high-speed railway systems that have urgent nondestructive testing requirements owing to aging and the associated potential for fatigue damage accumulation. This study presents a multi-feature integration and automatic classification algorithm for switch rail damage using guided wave monitoring signals. A combination of piezoelectric transducers and magnetostrictive patch transducers is adopted to improve the monitoring performance and meet actual monitoring requirements. Furthermore, multiple features extracted from various signal processing domains—such as the time domain, power spectrum domain, and time–frequency domain—are proposed and defined according to the structure and characteristics of the switch rail and guided wave to represent the complex nature of the damage. A damage index is defined to eliminate the influence of various environmental and operational conditions, signal power, and other factors. In addition, a feature selection method based on binary particle swarm optimization with a new fitness function is proposed to select the most damage-sensitive features and eliminate irrelevant and redundant features to improve the classification performance. Moreover, considering that the results are easily influenced by experts’ subjective judgment and experience, the least-squares support-vector machine is used to construct automatic classification models to reduce the probability of artificial incorrect diagnosis and improve the generalization ability to unknown environments. Finally, three types of experiments on the foot of a switch rail are presented to evaluate the proposed method. The results indicate that the proposed method is capable of identifying damage in challenging cases and is superior to conventional methods.
Magnetostrictive patch transducers (MPT) with planar coils are ideal candidates for shear mode generation and detection in pipe and plate inspection with the advantages of flexibility, lightness and good directivity. However, the low energy conversion efficiency limits the application of the MPT in long distance inspection. In this article, a method for the enhancement of the MPT was proposed by dynamic magnetic field optimization using a soft magnetic patch (SMP). The SMP can reduce the magnetic resistance of the magnetic circuit, which increases the dynamic magnetic field intensity in the magnetostrictive patch during wave generation and restricts the induced dynamic magnetic field within the area around the coils for sensing during wave detection. Numerical simulations carried out at different frequencies verified the improvement of the dynamic magnetic fields by the SMP and influence of different affecting factors. The experimental validations of the signal enhancement in wave generation and detection were performed in an aluminum plate. The amplitude magnification could reach 12.7 dB when the MPTs were covered by the SMPs. Based on the numerical and experimental results, the SMP with a large relative permeability and thickness and close fitting between the SMP and coils were recommended when other application conditions were met.
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