Shaking Noise Exploration and Elimination for Detecting Local Flaws of Steel Wire Ropes Based on Magnetic Flux Leakages

Abstract

Nowadays, steel wire rope (SWR) plays a more and more crucial role in modern facilities including but not limited to goods transmission, elevators, and dams. Due to the external environment, local flaws (LFs) on the SWR may cause deadly accidents. Thus, its health condition should be monitored. When applying the magnetic flux leakage (MFL) based nondestructive testing method to collect the LF signals in multichannel, the shaking noise inevitably exists, and greatly influences the accuracy of LF detection. This article focuses on analyzing shaking sources and the representation of shaking noise in MFL signals based on the lift-off distance. Then, after analyzing morphological features of shaking noise, strand noise, and LF signals, the morphological image processing based method is proposed to suppress shaking noise, especially when LF signals are covered and surrounded by strong shaking noise. In comparison with the state-of-the-art denoising method, this proposed method not only suppresses both strand noise and strong shaking noise but also improves the signal-to-noise ratio of MFL signals for better LF detection.