Vibrational Fatigue Analysis of a Strain Loading Using the Frequency and Wavelet Filtering Methods

Abstract

This paper presents a comparison work between the filtering methods of fatigue strain loadings using the frequency spectrum and the wavelet transform (WT), in which a raw loading signal can be simplified for purpose of simulation. For this reason, the Fast Fourier Transform (FFT) and the Morlet wavelet algorithms were used in order to transform the vibrational fatigue time series into the frequency domain signal, leading to the observation of the frequency characteristics of the signal. To retain high amplitude cycles in the FFT algorithm, a low pass filter technique was applied to remove the high frequency signals with small amplitude that are non-damaging. The departure of high frequency information smoothed the low amplitude cycles at high frequency events in the fatigue signal. The Butterworth filter was selected as the most efficient filter design as it retained most of the fatigue damage and also had the capability to remove 30 % of the original low amplitude cycles. On the other hand, the Morlet wavelet managed to remove 64 % of the original 59 second signal. This wavelet filtering method removed 34 % more than the similar procedure applied through the FFT approach. Hence, this fatigue data summarising algorithm can be used for studying the durability characteristics of automotive components.

Development of a Noise Filtering Algorithm for Strain Signals Using the Fast Fourier Transform

“…1. Strain signals measured at an automotive lower arm driven on paving block [10] and asphalt [11] were selected as the case study. The 46-second paving block strain signal was measured at a frequency of 500 Hz with 23,000 data points while the 160-second asphalt strain signal was measured at 200 Hz with 32,000 data points, as shown in Fig.…”