Issue 46

M. F. M. Yunoh et alii, Frattura ed Integrità Strutturale, 46 (2018) 84-93; DOI: 10.3221/IGF-ESIS.46.09 85 the components that are subjected to the fatigue loading. The fatigue time histories often contain a major percentage of small amplitude cycles, and the fatigue damage for these cycles can also be small. Therefore, in many cases, the fatigue loading history is edited by removing these small amplitude cycles to produce representative and meaningful, yet economical testing, Stephens et al. [2]. Several fatigue data editing techniques have been developed for use in the time domain analysis, Abdullah et al. [3]. Some previous algorithms have been developed for eliminating low amplitude cycles in order to only retain the high amplitude cycles, El Ratal et al. [4]. In the frequency domain, the time history is a low pass filtered by the criterion that high-frequency cycles have small amplitude, which is are not damaging. The filtering method does not shorten the signal because it does not provide the time-based information, Nizwan et al. [5]. In addition, Abdullah et al. [3] have developed a method for data editing to shorten the strain signal in the time-frequency domain. In fatigue data editing, the behaviour of extraction segments also needs to be studied because it contributes many bits of information that can improve fatigue life prediction. To deal with uncertainties and variations in fatigue data, the statistical analysis, i.e. the probability analysis is the best approach that should be adopted. Zhao and Liu [6] use the approach of statistical aspects of the S-N curve by means of the Weibull distribution. This approach indicates that an appropriate distribution determination is the primary task for a rolling contact fatigue analysis. Tiryakioglu [7] uses the Weibull analysis in fatigue data and predicts the failure mechanisms due to cracks initiating from surface and interior defects. The evaluation of fatigue damage extraction for automotive components under service loading is vital in the reliability analysis. Very few analysis methods have been developed to evaluate the fatigue damage extraction. A consistent description of the probability of damage occurrences is possible only if the damage distribution function is known. In this paper, two sets of strain signals from the real component in service are used in the fatigue feature extraction. The feature extraction, i.e. fatigue damage, is analysed using statistical inferences. The objective of this study is to determine the probabilistic-based failure of damage featured in the strain signals, and at the same time, validate them through the extraction process. By considering the significant statistical tools, features extraction is combined with the Weibull distribution analysis in order to obtain a better evaluation. T HEORETICAL BACKGROUND The Global Statistic he time series contains an explanation of a set of variables taken at equally spaced time intervals. A statistical analysis is normally used to determine the random signals and monitor the pattern of the analysed signals. The calculation of the root-mean-square (r.m.s.) and the kurtosis is very important in the fatigue signals in order to retain a certain number of the signal amplitude range characteristics. The r.m.s. value is the 2nd statistical moment, is used to quantify the overall energy content of the signal. For discrete data sets the r.m.s. value is defined as: (1) While kurtosis is the signal 4th statistical moment, is a global signal statistic which is highly sensitive to the spikiness of the data. For discrete data sets the kurtosis value is defined as: (2) where j x is the amplitude of signal, n is number of data and x is the mean value. The Wavelet Transform The wavelet transform (WT) is defined in the time-scale domain and is a significant tool for analysing the time-localised features of a signal. It represents a windowing technique within the variable-sized region. A wavelet transform can be classified as either a continuous wavelet transform (CWT) or a discrete wavelet transform (DWT) depending on the discretisation of the scale parameters of the analysing wavelet. The DWT based on such wavelet functions is called the orthogonal wavelet transform (OWT). Orthogonal wavelet transforms are normally applied for the compression and feature 2/1 1 2 1 . .          n j j x n smr       n j j x x smrn K 1 4 4 ). .( 1 T