Ensemble and individual noise reduction method for induction-motor signature analysis

Unlike a fixed-frequency power supply, the voltage supplying an inverter-fed motor is heavily corrupted by noises, which are produced from high-frequency switching leading to noisy stator currents. To extract useful information from stator-current measurements, a theoretically sound and robust de-noising method is required. The effective filtering of these noises is difficult with certain frequency-domain techniques, such as Fourier transform or Wavelet analysis, because some noises have frequencies overlapping with those of the actual signals, and some have high noise-to-frequency ratios. In order to analyze the statistical signatures of different types of signals, a certain number is required of the individual signals to be de-noised without sacrificing the individual characteristic and quantity of the signals. An ensemble and individual noised reduction (EINR) method is proposed as the extension of the common averaging method for induction-motor signature analysis. The signals after de-noising by the proposed EINR method will preserve the individual characteristics. A number of signals are selected as an ensemble part in the proposed EINR method and are employed as the “profile” to de-noise other individual signals. The case study presented in this paper demonstrates the merits of the proposed EINR method for induction-motor signature analysis.