DC Motor Fault Analysis with the Use of Acoustic Signals

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In the recent years, wavelet transforms have emerged as methods for many applications. Wavelets are formulated to describe signals in a localized time and frequency format. A linear combination of the shifted and scaled basis functions can be applied to model functions. These functions are defined in a space spanned by the wavelet family.
 In approaches based on FFT, windows are used uniformly for spread frequencies. In approaches based on wavelet transforms the short windows are used at high frequencies and the long windows are used at low frequencies. The adaptable window size is useful to su-pervise nonstationary disturbances. By using a wavelet transform, time and frequency information can be si-multaneously obtained (Huang, Hsieh, 2002).
 The efficiency of the fault analysis depends on the quality of the features selection. Wavelets can be used as features of the signal. They are used for diagnostics
of electrical motors (Abdesh Shafiel Kafiey Khan, Azizzur Rahman, 2010; Jawadekar et al., 2012; Stepien, Makiela, 2013; Stepien, 2014; Stepienet al., 2015).