Box-Cox enabled oscillatory sparse decomposition for frog feature extraction of high-speed railway turnout

Abstract High-speed railway turnouts are susceptible to failures due to the intricate structure. The interaction forces between wheels and rails reflect the health condition of turnouts. Hence, force signal analysis emerges as a robust technique for the turnout health assessment. A crucial aspect of this analytical process is the accurate identification of signal segments within the measured force data that are originated specifically from turnouts. This identification is vital as the locations of force signals measured often misalign with the recorded positions of the turnout. Given the sparsity of the force impacts induced by the frogs of turnouts, this paper proposes a Box-Cox enabled oscillatory sparse decomposition (BC-OSD) method. Given the sparse and low oscillatory nature of signal segments produced by frogs, the BC-OSD proves effective in isolating frog-generated features from complex force signals. BC-OSD employs Oscillatory Sparse Decomposition to partition the signal into two segments based on their oscillatory properties. The integration of the Box-Cox Sparse Measures enables the algorithm to adaptively adjust its performance of subbands selection based on the actual signal characteristics. The decomposition outcome relies upon the chosen parameters; thus, a systematic discussion on the parameter selection for processing wheel–rail force signals is provided. The efficacy of the proposed method is validated through the analysis of measured wheel–rail force signals.