Application of compressed sensing in the guided wave structural health monitoring of switch rails

Switch rails are weak but essential components of high-speed rail (HSR) systems. In the condition-based maintenance of HSR, ultrasonic guided wave (UGW) on-line monitoring technology is widely used in judging real-time operating conditions; however, it always generates large amounts of data. Too much data bring significant challenges, such as too many unnecessary costs of energy, storage, and network bandwidth in the structural health monitoring of switch rails, making it challenging to realize embedded sensor networks with high durability and low power consumption. Furthermore, the structural damage occurs relatively less over the long-term, indicating that these measurements are inherently sparse. The sparseness of structural damage makes them attractive for the compressed sensing technique. This study proposes a novel data compression and reconstruction method to meet the challenges and reduce the amount of data transmitted by sensor networks and maintain their accuracies simultaneously. First, a lightweight data dictionary is constructed to perform a sparse decomposition of UGW signals according to the characteristics of UGW propagation. Second, an effective sampling method based on a sparse random matrix is designed for sub-Nyquist sampling and compressing UGW signals. Third, a novel block adaptive matching pursuing algorithm is proposed to reconstruct UGW signals from compressed data. Finally, numerical signals, finite element simulation, and several actual monitoring experiments on the foot of a switch rail are conducted to verify the effectiveness and accuracy of the proposed method. The influence of different compression ratios and block sizes on the reconstruction performance of guided wave signals is investigated. The results indicate that the proposed method can sample UGW signals with much lower requirements than the Nyquist sampling theorem and is superior to other novel algorithms.