# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 4 Comparison and Results
### The New Proposed Method  
#### 4.3.1 Hilbert-Huang Transform Combined Lowpass Filter  
After further investigation for the weak wire rope inspection signals, it could be found that almost all the defect signals resemble the sinusoidal waveform in shape. Moreover, the defect signals always feature with a prominent amplitude in time domain and are surrounded with continuous strand waveform simultaneously. Simple time and frequency analysis may not be capable of recognizing these weak signals with complicated characterizations. Hence, a phase transform technique based on improved Hilbert-Huang Transform (HHT) and signal filtering methods are considered. According to the principles of the new proposed methods explained in Section 3, the same three typical weak signals processed through the proposed methods are presented in Figure 8.  
Specifically, the signals displayed in Figure 8a are the original weak signal S1, the Hilbert transformed signal, the differential signal, frequency spectrum. and the lowpass filtering signal in sequence. After the signal differentiation between the original signal and Hilbert transformed signal, the defect signal features become more remarkable, which could be observed from the third line box, despite that the differential signals being very weak and mixed with some noise. Furthermore, the frequency spectrum in the fourth line box shows that the differential signals are all in a low frequency band, and most of the components fall within the frequency region lower than 50 Hz. Therefore, further signal denoising techniques by Butterworth lowpass filtering method was applied, and when the cutoff frequency was 5 Hz, the processing results were shown in the last line box. Apparently, the defect signals were characterized with four obvious peak waves and the non-damaged signals were featured with a smooth baseline or a slight fluctuation, which manifests that the defect on the wire rope can be well inspected and distinguished. Comparing with the traditional nonlinear and linear denoising methods mentioned above, the proposed method is capable of weak wire rope signal inspection and shows great potential in high precision defect detection.  
Figure 7: Weak signals processed by moving average method. The original weak signals of 1 to 3 correspond with the testing signals of S1 to S3 in Figure 5.  
Figure 8: _Cont._  
The other two kinds of defect detection signals are expressed in Figure 8b,c, respectively. Similar to the processing results presented in Figure 8a, although the original signals of S2 and S3 are very weak both in amplitude and SNR, the basic defect features are preliminarily extracted after the signal differentiating, as shown in the third line box. According to the further frequency analysis by FFT, the frequency components and distribution of the differentiating signals presented in the fourth line box manifests that the processed signals are still featuring with low frequencies smaller than 100 Hz, especially for the defect characteristics gathering under 50 Hz. Judged from the lowpass filtering signal expressed in the last line box, five signal peaks with small burr noise could be observed obviously, namely, the weak signals could be distinguished preliminarily both for the original weak signals of S2 and S3. Comparing the signal processing results illustrated in Figure 8a with those expressed in Figure 8b,c, it could be deduced that the bigger the defect is, the higher the SNR and the smaller the signal span are. Consequently, further signal smooth filtering methods are required regarding weaker and bigger liftoff signals.