Durability Analysis for Coil Spring Suspension Based on Strain Signal Characterisation

 
 
 
  • Abstract
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  • Abstract


    This paper aims to predict the durability of an automobile coil spring by characterising the captured strain data. The load histories collected at coil spring are often presented in time domain but time domain cannot provide sufficient information for fatigue life prediction. The objective of this study was to characterise the strain signal in time domain, frequency domain and time-frequency domain for fatigue life prediction. The signal obtained in time domain was used to predict the fatigue life of the coil spring through Rainflow cycle counting technique and models of strain-life relationships. In frequency domain, fast Fourier transform revealed that the frequency components in the strain signal ranged between 0-5 Hz. The frequencies can be further categorised into two ranges: 0-0.3 Hz and 1-2 Hz. Power spectral density confirmed that the frequencies with high energy content were 0-5 Hz and the total energy content in the signal is 4.0872x103 µɛ2. Short time Fourier transform can identify the local time and frequency properties of the signal but it has a limitation in time-frequency resolutions. Wavelet transform can provide a better time-frequency resolutions and it confirmed that the transients in the time domain had frequency range of 1-2 Hz. In summary, this study revealed different possible approaches of signal processing in fatigue life assessment of automotive components as guidance for the selection of suitable approach based on the type of information needed for the analysis.

     

     


  • Keywords


    Time domain; Frequency domain; Time-frequency domain; Fatigue life; Durability

  • References


      [1] Jung DH & Gafurov A (2011), Reliability Achievement of the Driving System Parts through Development of Vibration-Fatigue Evaluation Method. Procedia Engineering, Vol. 10, pp. 1906-1916.

      [2] Petrucci G & Zuccarello B (2004), Fatigue Life Prediction under Wide Band Random Loading. Fatigue & Fracture of Engineering Materials & Structures, Vol. 27, No. 12, pp. 1183-1195.

      [3] Palmieri M, Česnik M, Slavič J, Cianetti F & Boltežar M (2017), Non-Gaussianity and Non-Stationarity in Vibration Fatigue. International Journal of Fatigue, Vol. 97, pp. 9-19.

      [4] Oh CS (2001), Application of Wavelet Transform in Fatigue History Editing. International Journal of Fatigue, Vol. 23, No. 3, pp. 241-250.

      [5] Mršnik M, Slavič J & Boltežar M (2013), Frequency-Domain Methods for a Vibration-Fatigue-Life Estimation – Application to Real Data. International Journal of Fatigue, Vol. 47, pp. 8-17.

      [6] Zhu K, Wong YS & Hong GS (2009), Wavelet Analysis of Sensor Signals for Tool Condition Monitoring: A Review and Some New Results. International Journal of Machine Tools and Manufacture, Vol. 49, No. 7–8, pp. 537-553.

      [7] Amin W, Davis MR, Thomas GA & Holloway DS (2013), Analysis of Wave Slam Induced Hull Vibrations Using Continuous Wavelet Transforms. Ocean Engineering, Vol. 58, pp. 154-166.

      [8] Abdullah S, Choi JC, Giacomin JA & Yates JR (2006), Bump Extraction Algorithm for Variable Amplitude Fatigue Loading. International Journal of Fatigue, Vol. 28, No. 7, pp. 675-691.

      [9] Mansor NII, Abdullah S, Ariffi AK & Syarif J (2014), A Review of the Fatigue Failure Mechanism of Metallic Materials under a Corroded Environment. Engineering Failure Analysis, Vol. 42, pp. 353-365.

      [10] Putra TE, Abdullah S, Schramm D, Nuawi MZ & Bruckmann T (2015), Generating Strain Signals under Consideration of Road Surface Profiles. Mechanical Systems and Signal Processing, Vol. 60–61, pp. 485-497.

      [11] Chen D, Fan J & Zhang F (2013), Extraction the Unbalance Features of Spindle System Using Wavelet Transform and Power Spectral Density. Measurement, Vol. 46, No. 3, pp. 1279-1290.

      [12] Yu S, You X, Ou W, Jiang X, Zhao K, Zhu Z, Mou Y & Zhao X (2016), STFT-Like Time Frequency Representations of Nonstationary Signal with Arbitrary Sampling Schemes. Neurocomputing, Vol. 204, pp. 211-221.

      [13] Kihm F & Delaux D (2013), Vibration Fatigue and Simulation of Damage on Shaker Table Tests: The Influence of Clipping the Random Drive Signal. Procedia Engineering, Vol. 66, pp. 549-564.


 

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Article ID: 16631
 
DOI: 10.14419/ijet.v7i3.17.16631




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