Magnetic Flux Leakage Sensing-Based Steel Cable NDE Technique
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A non-destructive evaluation (NDE) of long steel bridge cables is required to avoid structural damage during an inspection. Therefore, a robotic cable monitoring system using suitable NDE technology and a cable climbing robot is proposed. A magnetic leakage (MFL) based inspection system was used to evaluate the condition of the cable. This inspection system measures the magnetic flux to detect local faults (LF) in steel cables.
To test the feasibility of the proposed damage detection technology, a prototype 16-channel MFL sensor head was designed and manufactured. Various types of damaged steel cable bundle samples were prepared and scanned with an MFL sensor head to measure the magnetic flux density of the samples. To interpret the condition of the steel cable, the magnetic flux signal was used to identify the location of the defect and the extent of the damage. The measurement signal of the damaged sample was compared with the threshold value established for objective decision-making.
Specialized software was developed to locate loss of metallic area and corrosion to a level not seen before in the inspection community. This will allow asset owners to better budget for maintenance and repairs. The measured magnetic flux signal was visualized as a wave with specific indications to the inspectors in real-time to reflect any problem areas. To confirm the accuracy and effectiveness of the proposed cable monitoring method, we not only compared the results with information about the actual damage reported but opened up to compare percentage loss with software findings.
Recently, there has been an increasing demand for structural health monitoring (SHM) and non-destructive testing inspections (NDT) in civil engineering, mechanical engineering, and aerospace engineering. Many bridges quickly adopted SHM which can monitor bridge components after a negative event occurs. Here will discuss NDT inspections that better help monitors deterioration progression over time, giving asset owners more quantitative data to better manage the safety and longevity of infrastructure assets.
Bridges with long steel cables are just one important element where almost all of the structure’s dead loads are carried in the steel. However, corrosion and breakage can lead to cross-sectional damage to the steel cable, which leads to a concentration of stress. Cross-sectional damage can be a direct cause of structural deterioration. Therefore, a non-destructive evaluation (NDE) is required to capture the initial stages of cable cross-section damage. However, it is difficult to monitor the health of most cables as the damage can be invisible and inaccessible. To address these shortcomings, we propose a robotic cable inspection system that uses the appropriate NDE technology, in this case, magnetic flux leakage. Magnetic flux leakage is a technology that has been used for decades to locate section loss. Magnetic sensors are widely used to monitor structures such as aircraft and ships because of their excellent reliability and reproducibility. There are different types of magnetic sensors, and depending on the type of target structure, the optimal magnetic properties are available.
Most of these tests and inspections were manual in nature, choosing suspect areas and just inspecting those areas.
In this study, a magnetic sensor was used to detect cross-sectional damage.
In this study, an MFL sensor was used to detect loss of metallic area (LMA) damage to steel cables by detecting magnetic flux leaks. The magnetic flux leakage method is ideally suited for continuous structures with a constant cross-sectional area such as cables and pipes and is used for the inspection of steel cables in mining, ski lifts, elevators, and other applications. However, most MFL units are fixed systems and cannot be used with bridge steel cables. Besides, the measurement signal from the MFL device in the past required expert analysis to identify the damage. To overcome these limitations, advanced MFL-based damage detection techniques have been developed that use thresholds derived from statistical methods for objective decision-making. Prior to field testing, the units went through rigorous in-shop inspections on mock cables.
To test the feasibility of the proposed damage detection technology, a prototype 8-channel MFL sensor was designed and manufactured. Samples of steel cable bundles were also made experimentally and cuts were made in the cable to gradually develop cross-sectional damage. The sensor was used to measure the magnetic flux in any damaged condition. The measurement signal of the damaged sample was compared with the set threshold. Finally, the measured magnetic flux signal was visualized as a 3D MFL card for intuitive cable monitoring.
2. THEORETICAL BACKGROUND PRINCIPLE OF MAGNETIC FLUX LEAKAGE
Magnetized steel samples are surrounded by a magnetic field, and all the places where magnetic field lines enter and exit the sample are called poles. A cracked but not completely broken magnet will form the north and south poles at both ends of the crack. The magnetic field exists at the North Pole and re-enters the South Pole. Since air cannot support the magnetic field per unit volume that the magnet can, the magnetic field spreads when it hits a small air gap created by a crack. As the electric field spreads, it looks like it is leaking out of the material. Hence it is called an electric field with magnetic flux leakage.
Use strong permanent magnets or electromagnets to create magnetic flux in the material to be inspected. In the absence of defects, the flow in the metal remains uniform, as shown in the figure. In contrast, it shows a magnetic flux leak that occurs when LF damage occurs due to disruption or wear. Sensors that can detect this magnetic flux leakage are arranged between the poles of the magnet and generate an electrical signal that is proportional to the magnetic flux leakage.
In this study, we used a hall sensor to capture the MFL. The Hall sensor works based on the Hall Effect and is shown in Figure 4. When a magnetic field is applied to the plate, the electrons moving in the magnetic field experience a force perpendicular to both directions, known as the Lorentz force. In the direction of movement and field direction. It is the reaction to this force that creates the Hall voltage. This Hall voltage can be measured with the DAQ system and used to determine the location of the anomaly.
After measuring the magnetic flux, the appropriate threshold value for the measured output voltage must be determined, which differentiates between intact and damaged conditions? In this study, a generalized extreme value distribution (GEV) was used to establish a threshold value for the confidence level of 99.99% for the intact condition. According to the extreme value theorem, the GEV distribution is a well normalized maximum extreme distribution of a sequence of independent, identically distributed probabilistic variables. For this reason, the GEV distribution is used as an approximate value for modeling the maximum of long (finite) sequences of stochastic variables. The generalized extreme value distribution has a cumulative distribution function as shown below. Where are the parameters for the upper limit, position, scaling, and shape?
Infraspect, “Infraspect” developed an MFL sensor head and combined it with specialized software and robotics. The initial visualizations verified the feasibility of the proposed method. This was confirmed by the following observations.
- A leakage of magnetic flux was found in the damaged LF part.
- The detection sensitivity depends on the damage and the distance between the Hall sensors.
- By installing the sensors in an array, the position of the damage in the circumferential direction can be determined.
- The magnetic flux leakage signal exceeds the threshold based on the GEV distribution at the actual point of damage.
- The MFL signal is represented by a wave signal showing the size and location of the LMA damage at a glance.
- The MFL signal was represented by visualizing the threshold level by mapping a diagram onto a screen showing very specific levels of loss of metallic area.
This MFL-based cable NDT technology has been integrated into a number of cable climbing robots to perform very specific infrastructure inspections. The technology far surpasses results by current field methods and is ideal for planning budgets and maintenance repairs.
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- Dangerous to the worker
- Subjective visual inspection
- Cannot view 360 degrees of guy wire
- Does not inspect the entire cable
- Does not provide deterioration progression over time.
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- Locates loss of metallic area /corrosion
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- Provide deterioration progression over time.
- Provides quantitative data
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