Diagnosis of Friction on an Unbalanced Rotor by Phase-Shift Empirical Mode Decomposition Integration and Recurrence Plot

Abstract

The friction and imbalance of components in rotating machines are some of the most recurrent failures that significantly increase vibration levels, thus affecting the reliability of the devices, the shelf life of its elements, and the quality of the product. There are many publications related to the different techniques for the diagnosis of friction and imbalance. In this paper, an alternative and new phase-shift empirical mode decomposition integration (PSEMDI) method is proposed to transform the acceleration into its velocity and displacement in order to construct the phase plane and recurrence plot (RP) and analyze the friction. The focus of PSEMDI and RP is to analyze nonlinear failures in mechanical systems. In machinery fault diagnosis, the main reason for using RP is to solve the integration of acceleration, and this can be achieved by phase-shifting the intrinsic mode function (IMF) with the empirical mode decomposition (EMD). Although the highest IMFs contain some frequencies, most of them have very few; thus, by applying the phase shift identity, the integration can be carried out maintaining the nonlinearities. The proposed method is compared with Simpson’s integration and detrending with the EMD method (here referred to as SDEMDI). The experimental RP results show that the proposed method gives significantly more information about the velocity and displacement spectra and it is more stable and proportional than the SDEMDI method. The results of the proposed integration method are compared with vibration measurements obtained with an interferometer.

Perspective Chapter: Predicting Vehicle-Track Interaction with Recurrence Plots

“…Torres and Jauregui demonstrated that numerical integrations introduce artificial errors in the phase plane [4]. They proposed a different integration procedure to avoid these errors that combine the one-quarter shifting method and the empirical mode decomposition (EMD) [5]. The EMD adapts the time domain to process nonstationary and nonlinear time series.…”

“…Torres et al [4] analyzed the error caused by applying the shifting process to nonlinear signals. To avoid this error, Torres et al [5] proposed a different alternative. This procedure is further discussed in the following sections.…”

“…The data were obtained in an experimental rig, and they consisted of acceleration and velocity measurements registered at the railcar. The acceleration was integrated using the empirical mode decomposition method and the shifting property of periodic functions [5]. Results can be extrapolated to real measurements.…”