Wavelet-Based Steel Wire Rope Inspection

Steel wire ropes, such as those used in cranes, elevators, and rubber conveyor belts, are one of the most critical parts in heavy machines. The hidden flaws in steel wire ropes, such as corrosion, abrasion, or partially broken wires, can lead to catastrophes and substantial loss of lives and property. This application note introduces how you can use National Instruments (NI) data acquisition (DAQ) boards, NI LabVIEW, and the wavelet analysis tools in the LabVIEW Advanced Signal Processing Toolkit 7.5 to detect defects in steel wire ropes and prevent serious accidents. The system introduced in this application note was developed by Shanghai Qiehua Virtual Instruments Corp.

Products: NI PCI-6225, LabVIEW, and the Advanced Signal Processing Toolkit 7.5

Problems: The reliable and safe usage of steel wire ropes is crucial for many applications, ranging from construction and mining fields to offshore applications. The hidden flaws in steel wire ropes, such as corrosion, abrasion, or partially broken wires, can lead to catastrophes and substantial loss of lives and property. Periodically checking the rope condition ensures safety. However, many ropes deteriorate internally, meaning without any externally visible signs, visual inspection is often difficult and not practical.

Many flaws often change the metallic cross-sectional area of the ropes. The metallic cross-sectional area is proportional to magnetic flux. Therefore, you can determine hidden flaws by measuring the magnetic flux with sensors. Theoretically, you can use the measured magnetic flux information to deduce the loss of metallic cross-sectional area (LMA) values and use these values to evaluate the condition of the rope. Figure 1 illustrates the physical status of a rope and the ideal measured signal. Because of abrasions, some wires are missing in the middle of the rope.

Figure 1. A Broken Steel Wire Rope and the Ideal Measured Signal

The position of the valley in the measured signal can help locate the flaw. The amplitude of a signal valley can help determine the LMA value. The LMA value is proportional to the number of missing wires. Figure 2 illustrates a steel wire rope inspection device. This device scans the steel wire rope and uses sensors to acquire the magnetic flux information of the cross-sectional area of the rope.

Figure 2. A Steel Wire Rope Inspection Device

Compared to the ideal measured signal in Figure 1, signals acquired from real-world applications are much more noisy and complicated. Figure 3 shows a typical measured signal that contains multiple valleys. The valleys indicate that some wires are broken at the corresponding positions. The valley at 50 on the x-axis implies a more severe condition because this valley is wide and the amplitude of the valley is large. Therefore, some wires might be missing at this position.

Figure 3. A Typical Measured Signal after Detrending

Note: This example uses the wavelet-based detrending method to preprocess the measured signal.

A good steel wire rope inspection system must detect the positions and amplitudes of the valleys accurately. Traditional instruments use the curve-fitting-based peak or valley detection method, which is sensitive to noise. You cannot rely on the results of the curve-fitting-based method and usually need the help of experienced engineers to verify the results.

Solution and Result: Compared to the curve-fitting-based method, the wavelet-based peak or valley detection method is much less sensitive to noise. Figure 4 compares the valleys detected by the wavelet-based and curve-fitting-based methods. The curve-fitting-based method detects only three valleys in the signal. The wavelet-based method detects all the five valleys in the signal with more exact positions and amplitudes.

Figure 4. Comparing the Wavelet-Based and Curve-Fitting-Based Methods

By applying the wavelet-based peak or valley detection method, the steel wire rope inspection system, developed by Shanghai Qiehua Virtual Instruments Corp, can detect and estimate the flaws automatically and accurately in various types of steel wire ropes. The new product has received great feedback from customers.

Contributed by:
Eric Xiang and Kevin Wang
National Instruments, Austin, Texas
Shanghai Qiehua Virtual Instruments Corp

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