Empirical mode decomposition, an adaptive approach for interpreting shaft vibratory signals of large rotating machinery.

The Fourier transform (FT) has been the most popular method for analyzing large rotating machine shaft vibration problems, but it assumes that these vibration signals are linear and stationary. However, in reality this is not always true. Nonlinear and non-stationary shaft vibration signals are often encountered during the start-up and shut-down processes of the machines. Additionally, mechanical faults, for example rotor-to-stator rubbing, fluid excitation, part-loosening, and shaft cracking, are nonlinear. Owing to these reasons, an accurate analysis of shaft vibration cannot always be achieved by using the FT. An alternative tool, the wavelet transform (WT), is now being used to improve the situation. But the efficiency is a problem especially when applying the WT to the accurate analysis of a large-scale, lengthy data. In view of the powerful capability of empirical mode decomposition (EMD) to process nonlinear/non-stationary signals, its algorithm efficiency and its satisfactory performance in minimizing energy leakage, the EMD is used in this paper to analyze the problem, the signals investigated are adaptively decomposed into a finite number of intrinsic mode functions (IMFs). The principal IMFs, identified using an energy-distribution threshold, dominate the signals’ oscillation. So, ‘purified’ shaft vibration signals can be reconstructed from these principal IMFs. To remove interference present in principal IMFs, an adaptive band-pass filter is designed, whose central frequency is automatically set to the frequency dominating the IMF being investigated. To facilitate the observation of transient shaft vibration, a transient shaft orbit (TSO) is constructed by introducing timescale into the orbit drawing process. Nine mathematical criteria are also proposed to evaluate the shaft vibrations exhibited in the IMFs and TSOs. The novelty of this approach is that the EMD provides an adaptive, effective, and efficient way to obtain ‘purified’ shaft vibration signals, which describe the transient shaft vibration more vividly and precisely, reducing misinterpretation of the machine running condition. The approach is validated by two practical examples: • Part-loosening incident on a large centrifugal compressor. • Fluid excitation incident on a centrifugal compressor. Calculations of the mathematical criteria for seven different running conditions of these large rotating machines are also presented. It is demonstrated that the proposed technique provides a feasible and reliable way to interpret shaft vibration, by which means the machine condition can be diagnosed correctly