Paper Title
One-Shot Damage Detection of Steel Wire Ropes Byactive Sound Analysis

Abstract
Steel wire ropes are widely used in industries for lifting and conveying heavy loads. Some steel wire ropes are in an idle standby state in most of time, with only rare occasions when they are activated. Due to limited manpower, only loosely inspection will be performed and, therefore, any breakage could lead to severe injuries or fatalities. Existing automatic approaches mostly are performed inch-by-inch and still are time- and effort- consuming. This study investigates a hybrid damage detection algorithm with a clear goal that the inspection must be performed in one-shot. By means of our algorithm, damage localization in multi wire cables is performed through a single operation of the injection sound wave packets. Our aim in smart steel cable inspection strives for performing non-destructive, one-shot assessments using a portable device operated by a single individual. This inspection performs during the relaxed state when the cables are not actively in use. By leveraging both physical principles and artificial intelligence analysis, the system detects potential damages. The goal is to swiftly and comprehensively examine entire cable lengths at once, avoiding the labor-intensive process of inch-by-inch inspection. After batch operations, personnel can update inspection records and determine the next maintenance schedule based on the cable’s health. The algorithm is fully automated in terms of injected wave packets. To overcome the limitation of data collection in lifting floors, a novel semi-supervised transfer learning is proposed for extending the applicability of laboratory simulation data. The method comprises a generative model with the second harmonic waves on a latent space and a cluster algorithm on deviation. We have validated our proposed method with a recorded set of break-out events, and the estimated anomaly score is closely matched the rope condition. The false alarm rate turned out to be under 2%. Finally, for cross-validation based on new field data, the false alarm rate can still be maintained under15%. Keywords - sound waves, Barkhausen effect, damage detection and localization, signal processing, nonlinear waves, shear wave sounds, Robust Boost, boost regression, dual-tree complex wavelet, dynamic spatial warping