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.

Highlights

  • There is much research related to fault diagnosis in machinery and most studies are based on the linear dynamics of physics

  • This paper aims to address the suitability of recurrence plot (RP) to diagnose some classical mechanical faults of the nonlinear friction phenomenon and to propose a new alternative method to integrate the acceleration that keeps the nonlinearities of the vibration signal

  • The friction force was the lowest (0.09 N), it increased with speed up to 1400 rpm (0.88 N)

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Summary

Introduction

There is much research related to fault diagnosis in machinery and most studies are based on the linear dynamics of physics. If the diagnosis could include linear and nonlinear phenomena, a better understanding and failure prediction could be obtained to manage and control faults in machinery. The industry has implemented various maintenance strategies throughout history, and conditioning monitoring (CM) has been the most successful. CM is based on monitoring vibrations to control machinery conditions and implementing corrective actions by applying a scheduled maintenance program [1,2]. Identifying a nonlinear phenomenon in a rotating operating machine is not an easy task because of the complexity of the techniques typically used in CM that are based on theoretical and simplified assumptions; a better characterization of the nonlinear behavior can significantly improve failure diagnosis

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