Multiple wire twisted steel strands are commonly used to hoist elevators, concrete structures, etc. Due to frequent and long-time usage, the steel strands are subjected to corrosion, overloads, and aging, making strands may fail unexpectedly. Hence, the health monitoring of steel strands becomes more important to avoid the sudden collapse of hoisting structures. Guided waves (GW) inspection methods have become favorable in recent years due to its long-distance transmission and stability of evaluation in the area of structural health monitoring (SHM) and Non-Destructive Testing (NDT). Many researchers have reported different GW methods to detect different types of defects that occurred in steel strands. However, researchers rarely carry out comparative studies to investigate the effectiveness of each method or system in monitoring the health state of steel strands. This article reports some vital observations revealed from conducting experiments by using contact and noncontact methods, which include three different popular types of GW sensors and methods during their applications in surface-type defect detection. The proper selection of sensors systems has been identified through the present study. The result of the present study is believed to be useful guidance for selecting appropriate GW methods and sensor systems to monitor the integrity of the steel strand and thereby ensure the safety of the hoisted structures.

Experimental Investigation on Choosing a Proper Sensor System for Guided Waves to Check the Integrity of Seven-Wire Steel Strands.

In order to compare the thermal and electric field distribution inside the 110 kV smooth aluminum sheathed cable and the corrugated aluminum sheathed cable, an electro-thermal coupling model of the 110 kV corrugated aluminum sheathed cable and the smooth aluminum sheathed cable is established in this paper by means of finite element simulation. The calculation results show that the load capacity of the smooth aluminum sheathed cable is greater than that of the corrugated aluminum sheathed cable, and the internal thermal and electric fields of the smooth aluminum sheathed cable are more uniformly distributed without electric field distortion. The existence of the air gap increases the thermal resistance inside the corrugated aluminum sheath, making the temperature difference between the inside and outside of the corrugated aluminum sheath cable larger and increasing the thermal energy dissipation. Moreover, the junction of the air gap and the metal sheath increases the electric field strength.

Coupled Thermal-Electrical Analysis of 110 kV Corrugated Aluminum Sheathed and Smooth Aluminum Sheath Cables Based on Finite Element Simulation Method

High-voltage cable accessories are important parts of a power system and are needed to ensure the reliability of electrical connections. However, their aluminum sheaths are prone to corrosion under complex working conditions, which has a detrimental effect on the normal operation of the electric system. Ultrasonic guided wave detection is a promising non-destructive testing method suited to detecting aluminum sheath corrosion in complex high-voltage cable accessory structures. However, current ultrasonic guided wave detection methods still require manual extraction of the signal features and have a high reliance on professional knowledge. This paper proposes a deep learning-based corrosion-like defect localization technique for high-voltage cable aluminum sheaths using guided waves. First, the original ultrasonic guided wave signals of corrosion defects at different locations are obtained using an ultrasonic guided wave detection platform. Then, the original signals are input into a variable auto-encoder (VAE) network to obtain a low-dimensional representation for automatic feature extraction. Finally, the low-dimensional representation is input into a gated recurrent unit (GRU) based recurrent network for corrosion defect localization. In the feature extraction stage, the VAE can automatically extract the effective features and avoid the interference of noisy signals. In the defect localization stage, the GRU can accurately identify the location of corrosion defects. The experimental results indicate that the VAE-GRU method is capable of accurately identifying corrosion defects based on the original signals.

Deep learning-based corrosion-like defect localization technique for high-voltage cable aluminum sheaths using guided waves

The method of fast non-destructive testing for aluminium sheaths of high-voltage (HV) cables using ultrasonic-guided waves is proposed. The dispersion curve is calculated by solving the dispersion equation from the simplified model of aluminium sheaths of HV cables; thus, the appropriate wave mode and excitation frequency can be chosen. Three defects including crack, perforation, and sag have been set up. Then, the simulation experiments have been carried out for aluminium sheaths of HV cables based on the theoretical analysis and finite element simulation. The results show that cracks with the loss ratio of the sectional area over 4.25% can be detected by the T (0, 1) mode whose frequency is 32 kHz; the amplitude of the flaw echo increases along with the loss ratio of the crack. Theoretical analysis, finite element simulation, and experiments prove the feasibility of aluminium sheaths using ultrasonic-guided waves, providing a new method for the defect testing.

Yang Yong

Papers

Research of non-destructive testing for aluminium sheaths of HV cables using ultrasonic-guided waves