###### Abstract  
Structural health monitoring (SHM) is vital to the maintenance of civil infrastructures. For rail transit systems, early defect detection of rail tracks can effectively prevent the occurrence of severe accidents like drailment. Non-destructive testing (NDT) has been implemented in railway online and offline monitoring systems using state-of-the-art sensing technologies. Data-driven methodologies, especially machine learning, have contributed significantly to modern NDT approaches. In this paper, an efficient and robust image classification model is proposed to achieve railway status identification using ultrasonic guided waves (UGWs). Experimental studies are conducted using a hybrid sensing system consisting of a lead-zirconate-titanate (PZT) actuator and fiber Bragg grating (FBG) sensors. Comparative studies have been firstly carried out to evaluate the performance of the UGW signals obtained by FBG sensors and high-resolution acoustic emission (AE) sensors. Three different rail web conditions are considered in this research, where the rail is: (1) intact without any defect; (2) damaged with an artificial crack; and (3) damaged with a bump on the surface made of blu-tack adhesives. The signals acquired by FBG sensors and AE sensors are compared in time and frequency domains. Then the research focuses on damage detection using a convolutional neural network (CNN) with the input of RGB spectrum images of the UGW signals acquired by FBG sensors, which are calculated using Short-time Fourier Transform (STFT). The proposed image classifier achieves high accuracy in predicting each railway condition. The visualization of the classifier indicates the high efficiency of the proposed paradigm, revealing the potential of the method to be applied to mass railway monitoring systems in the future. rail defect detection; image classification; ultrasonic guided wave; fiber Bragg grating; convolutional neural network; Short-time Fourier Transform +
Footnote †: journal: Applied  
Therefore, an early-in-time and on-site diagnosis should be implemented to monitor the safety status of rails. With more attention paid to the maintenance of the urban and intercity railway systems, inspection vehicles and manual inspection have been widely utilized to detect early-stage defects. These approaches are capable of reporting thorough details of the rail condition, but they can either be labor costly or lack time efficiency for long-range monitoring projects. Therefore, non-destructive testing (NDT) has been applied, using ultrasonic detecting devices [5], optical fiber sensors [6; 7; 8], piezoelectric sensors [9; 10; 11] and even onsite cameras [12; 13] to conduct online monitoring. Such sensing technologies have the distinguished advantage that they can acquire data while not interrupting the operation of the railway systems. For instance, researchers have studied image- and video-based inspection methods using digital cameras installed on the operating trains or fixed on-site aided with computer vision (CV) [12], yet the limited hardware performance prevents these methods from being widely utilized to detect minor or inner defects on the rail tracks. On the other hand, ultrasonic bulk wave-based devices can inspect most abnormalities within the sensing range and have also been utilized on the rail track routine inspections [14]. However, ultrasound actuators and transducers are required to be installed on the tracks, which inevitably brings inconvenience considering that such a monitoring approach can be time-consuming and even insecure in some cases. Similarly, acoustic emission (AE) technology, despite its high resolution in receiving ultrasound [15; 16; 17], has the same limitation for the deployment in damage detection of rail segments. Moreover, it is generally assumed that AE sensors are mostly applicable when ultrasonic waves are generated due to the crack growth within the object, which makes it extremely difficult for engineers to design a long-range monitoring system based on such technologies.