LAUSR

Steel wire ropes are used for transporting personnel and materials in coal mines. Its quality directly affects the safety of mining operations, and hence, it is important to accurately identify the defects. Since electromagnetic detection methods are affected by large amounts of noise, more precise strategies are required. This article proposes a noise reduction method based on K-singular value decomposition (K-SVD) optimized double-tree complex wavelet transform (DTCWT). First, the discrete coefficients of the evaluation index of different decomposition levels in DTCWT are calculated to obtain the optimal decomposition levels. Afterward, K-SVD is used to update the sparse dictionary. The decomposed high-frequency modes were optimized for noise reduction. Finally, DTCWT was used to reconstruct the damage detection signal from the wire rope, after noise reduction. The experimental results show that the signal-to-noise ratio obtained using the proposed method is 30% higher than the other algorithms. The smoothing coefficient obtained is lower than 0.01, whereas the correlation coefficient is found to be more than 0.95, which indicates that the proposed algorithm can effectively reduce the noise in the detection signal and significantly retain its original characteristics, thereby improving the safety of the wire rope.