Early diagnosis in rotating machinery has been a challenge when looking toward the concept of intelligent machines. A crucial and critical component in these systems is the lubricated journal bearing, subjected to wear fault by abrasive removing of material in its inner wall, mainly during run-ups and run-downs. In extreme conditions, wear faults can cause unexpected shutdowns in rotating systems. Consequently, advanced condition monitoring is an essential procedure in the wear diagnosis of journal bearings. Although an increasing number of data-driven condition monitoring approaches for rotating machines have been proposed in the past decade, they mostly rely on substantial amounts of experimental data for training, which is expensive and time-consuming to obtain. The objective of this work is to develop a framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings using simulated vibrations signals. Numerically simulated data sets under different wear severity levels and operating conditions were used to train and test the diagnostics framework. The results show that the proposed framework can be a promising tool to diagnose wear faults in journal bearings.

In rotor dynamics, a traditional way of representing the dynamics of hydrodynamic bearings is using stiffness/damping coefficients. It is thus necessary to carry out a linearization of hydrodynamic forces around the shaft’s equilibrium position. However, hydrodynamic bearings have highly nonlinear nature, depending on operating conditions. Therefore, this paper discusses the applicability of these linear/nonlinear approaches using a computational model of the rotating system, where the finite element method is used for rotor modelling and the finite volume method for bearing calculation. The main goal is to investigate the boundaries for linear approximation of the hydrodynamic forces present in lobed hydrodynamic bearings, with the system operating under high loading conditions. Several numerical simulations were performed varying preload parameter and rotating speed. A comparison of the system’s responses, in time domain (shaft orbits) and frequency domain (full spectrum), is made for linear and nonlinear models. Results showed that trilobed bearings are more susceptible to nonlinearities, even in situations of smaller vibration amplitudes, while elliptical bearings are sensitive only under larger vibration amplitudes. These analyses are of great importance for mapping the influence of nonlinearities in different types of lobed hydrodynamic bearings with fixed geometry, varying the preload parameter to verify the influence on the system’s dynamic response. This study is important and serves as the basis for cases of monitoring and fault diagnosis (in the field of structural health monitoring) since it is crucial to distinguish what would be a fault signature or a standard nonlinear effect created by the use of hydrodynamic bearings.

Recent research into machines involved in power generation processes has demanded deep investigation of model-based techniques for fault diagnosis and identification. The improvement of critical fault characterization is crucial in the maintenance process effectiveness, hence in time/costs saving, increasing performance and productivity of the whole system. Consequently, this paper deals with a common fault in hydrodynamically lubricated bearings assembled in rotating systems, namely, that of abrasive wear. Research on this topic points to an interesting query about the significance of model detail and complexity and the identification of its characteristic parameters for the important stages of fault diagnosis and fault identification. For this purpose, two models are presented and analyzed in their completeness concerning the fault signature by vibration measurements, as well as the identification of fault critical parameters which determine the machine lifetime estimation, maintenance procedures, and time costs regarding performance and productivity. From this study, the detailing in fault modeling has a substantial impact on fault parameter identification, even if its improvement is not so expressive in fault diagnosis procedures involving standard signal processing techniques of vibration signatures.