In high-speed rail (HSR) condition monitoring, the conflict between the resolution of defect detection and the amount of recorded data is usually an issue due to the Nyquist theorem. As an emerging technique, compressive sensing (CS) creates the opportunity of sub-Nyquist sampling when target signals have a sparse representation in a known domain. However, many studies have shown that the lack of sparsity limits the applicability of CS. In addition, when multiple compressed measurement vectors are available, conventional CS algorithms recover target signals one at a time independently without exploiting the correlation among their sparse representations. This study applies CS to HSR condition monitoring and employs two methods to improve the recovery accuracy. Specifically, the process of CS is simulated using the axle box acceleration data acquired from a high-speed train ran on one section of railway in China. After the investigation of recovery results, the same experiments are conducted, except that the discrete cosine transform (DCT) matrix is replaced by a redundant dictionary. Another series of experiments assume that the signals have a joint sparsity in the DCT domain and reconstruct them simultaneously. The results show that the HSR condition monitoring data can be obtained through sub-Nyquist sampling and reconstructed with small errors when they are sufficiently sparse. Even if the compressed measurements are the same, both methods are proved effective to improve the recovery performance, in which joint reconstruction has better performance than the other.
|Publication status||Published - 1 Jan 2018|
|Event||9th European Workshop on Structural Health Monitoring, EWSHM 2018 - Manchester, United Kingdom|
Duration: 10 Jul 2018 → 13 Jul 2018
|Conference||9th European Workshop on Structural Health Monitoring, EWSHM 2018|
|Period||10/07/18 → 13/07/18|
ASJC Scopus subject areas
- Civil and Structural Engineering