Advanced signal processing methods applied to guided waves for wire rope defect detection

Abstract

Steel wire ropes, which are usually composed of a polymer core and enclosed by twisted wires, are used to hoist heavy loads. These loads are different structures that can be clamshells, draglines, elevators, etc. Since the loading of these structures is dynamic, the ropes are working under fluctuating forces in a corrosive environment. This consequently leads to progressive loss of the metallic cross-section due to abrasion and corrosion. These defects can be seen in the forms of roughened and pitted surface of the ropes, reduction in diameter, and broken wires. Therefore, their deterioration must be monitored so that any unexpected damage or corrosion can be detected before it causes fatal accident. This is of vital importance in the case of passenger transportation, particularly in elevators in which any failure may cause a catastrophic disaster. At present, the widely used methods for thorough inspection of wire ropes include visual inspection and magnetic flux leakage (MFL). Reliability of the first method is questionable since it only depends on the operators’ eyes that fails to determine the integrity of internal wires. The later method has the drawback of being a point by point and time-consuming inspection method. Ultrasonic guided wave (UGW) based inspection, which has proved its capability in inspecting plate like structures such as tubes and pipes, can monitor the cross-section of wire ropes in their entire length from a single point. However, UGW have drawn less attention for defect detection in wire ropes. This paper reports the condition monitoring of a steel wire rope from a hoisting elevator with broken wires as a result of corrosive environment and fatigue. Experiments were conducted to investigate the efficiency of using magnetostrictive based UGW for rope defect detection. The obtained signals were analyzed by two time-frequency representation (TFR) methods, namely the Short Time Fourier Transform (STFT) and the Wavelet analysis. The location of the defect and its severity were successfully identified and characterized.