Issue 46

M. F. M. Yunoh et alii, Frattura ed Integrità Strutturale, 46 (2018) 84-93; DOI: 10.3221/IGF-ESIS.46.09 89 Data N (Cycles) r.m.s (με) Kurtosis Fatigue Damage (Damage/Block) SAESUS 1253 246.6 4.28 CF Morrow SWT 1.70 x 10 -3 1.70 x 10 -3 1.64 x 10 -3 S1 9768 122.68 2.82 CF Morrow SWT 2.97 x 10 -4 2.94 x 10 -4 2.85 x 10 -4 Table 1 : Summary of statistical characteristic of the signals The developed wavelet transform algorithm transformed the signal into a time-frequency domain to obtain the time- frequency localisation, as shown in Fig. 3. The spectrum colour intensity is proportional to the absolute energy coefficients values because it delivers the energy distribution display on time and frequency. The spectrum of SAESUS and S1 signals shows that the intensity colours of the SAESUS strain signal are more highlighted compared to S1. A large scale was indicative of low frequency, and higher amplitude segments indicated that the cycles had higher energy. This means that it can inflict higher fatigue damage. (a) (b) Figure 3 : The wavelet transform spectrum of; (a) SAESUS, (b) S1. Higher amplitude segments based on wavelet transform spectrum were extracted. The location of the retained segments was shown in Fig. 4. After eliminating the low amplitude segments, the high amplitude segment should be retained to maintain the fatigue damage contained in both signals. The SAESUS strain signal contributed 21 high amplitude retained segments. Meanwhile, the S1 strain signal contributed 11 high amplitude retained segments. Based on the extraction results, the SAESUS strain signal consists of higher amplitude segments compared to S1 because it retained the higher amplitude segments after the extraction process. (a) (b) Figure 4 : The location of the retained segments; (a) SAESUS, (b) S1. The features extraction, i.e. fatigue damage for every segment, was calculated to analyse and determine the probability of failure based on high amplitude segments. It is helpful to know the fatigue damage distribution based on the extracted segments for both signals. The 2-P Weibull distribution was used for the data analysis. According to Tab. 2, the values of the shape parameters, β , for both signals were less than 1.0, indicating a decreasing failure rate. According to Finkelstein, [19], if the shape parameter for Weibull distribution is less than 1.0, this distribution is always decreasing the failure rate. The value of scale parameter, θ , of fatigue damage for both signals was found to be 5.3 x 10 -5 and 1.4 x 10 -5 . This means that when the components reach these values, the probability of failure will increase. Data Scale (1/Hz) 0.5 1 1.5 2 2.5 x 10 4 1 27 53 79 105 131 157 183 209 235257 50 100 150 200 250 Data Scale (1/Hz) 1 2 3 4 5 6 x 10 4 1 27 53 79 105 131 157 183 209 235257 50 100 150 200 250 0 20 40 60 80 100 120 -1,000 -800 -600 -400 -200 0 200 400 600 800 1,000 Amplitude (ue) Time (s) 0 20 40 60 80 100 120 -500 -400 -300 -200 -100 0 100 200 300 400 500 Amplitude (ue} Time (s)