Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method

  • Authors

    • A A. Rahim
    • C H. Chin
    • S Abdullah
    • S S. K. Singh
    • M Z. Nuawi
    • F H. A. Hassan
    2018-08-01
    https://doi.org/10.14419/ijet.v7i3.17.16642
  • Fatigue life, frequency domain, k-mean clustering, time-frequency domain, wavelet decomposition.
  • This paper aims to study the characterisation of time-frequency domain to analyse the fatigue strain signal due to weaknesses in time domain and frequency domain approaches. The objectives were to determine the behaviour of strain signal, characterise the fatigue life of strain signal and validate the fatigue life in time-frequency domain. The strain signal was obtained using data acquisition devices and strain gauges on two types of road condition including highway and industrial area. The acquired signals were analysed with time domain, frequency domain and time-frequency domain approaches. In time-frequency domain, the signals were decomposed using 4th Daubechies discrete wavelet transform. To validate the effectiveness of time-frequency approach in characterising vibration fatigue signal, fatigue data was clustered by mapping of the data based on the spectrum energy, root-mean-square and fatigue life obtained. The clustering was performed by comparing the centroid values which both data had five clusters as the optimum data clustering with 0.836 average distance to centroid. From this, the relationship between fatigue life, root-mean-square and spectrum energy can be determined and thus a new fatigue life criterion was developed.

     

  • References

    1. [1] Patil R, Ingawale S, Rupnar R & Tupake S (2015), Design and analysis of independent suspension system using FEA. International Journal of Engineering Research & Technology 4(4), 769–775.

      [2] Al-Asady NA (2009), Comparison between experimental road data and finite element analysis data for the automotive lower suspension arm. European Journal of Scientific Research 29(4), 557– 571.

      [3] Lei Y, He Z & Zi Y (2009), Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing 23(4), 1327-1338.

      [4] Bendjama H, Bouhouche S & Boucherit MS (2012), Application of wavelet transform for fault diagnosis in rotating machinery. International Journal of Machine Learning and Computing 2(1), 82–87.

      [5] Putra TE, Abdullah S, Schramm D, Nuawi MZ & Bruckmann T (2017), Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and the fuzzy c -means. Mechanical Systems and Signal Processing 90, 1–14.

      [6] Oh C (2001), Application of wavelet transform in fatigue history editing. International Journal of Fatigue 23, 241–250.

      [7] Apetre N, Arcari A, Dowling N, Iyyer N & Phan N (2015), Probabilistic model of mean stress effects in strain-life fatigue. Procedia Engineering 114, 538–545.

      [8] Medina-Daza RJ, Vera-Parra NE & Upegui E (2017), Wavelet daubechies (db4) transform assessment for worldview-2 images fusion. Journal of Computers 12(4), 301-308.

      [9] Santagati S, Bolognini D & Nascimbene R (2012), Strain life analysis at low-cycle fatigue on concentrically braced steel structures with rhs shape braces. Journal of Earthquake Engineering 16, 107–137.

      [10] Yahya MM, Mallik N, & Chakrabarty I (2015), Life prediction of low cycle fatigue behavior in rotating cantilever beam of Al- Alloy AA 6063-T6 at room temperature. International Journal of Emerging Technology and Advanced Engineering 5(11), 95–103.

      [11] Liberatore S & Carman GP (2004), Power spectral density analysis for damage identification and location. Journal of Sound and Vibration 274, 761–776.

      [12] Napoleon D & Pavalakodi V (2011), A new method for dimensionality reduction using k-means clustering algorithm for high dimensional data set. International Journal of Computer Applications 13(7), 0975 – 0987.

  • Downloads

  • How to Cite

    A. Rahim, A., H. Chin, C., Abdullah, S., S. K. Singh, S., Z. Nuawi, M., & H. A. Hassan, F. (2018). Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method. International Journal of Engineering & Technology, 7(3.17), 158-162. https://doi.org/10.14419/ijet.v7i3.17.16642