Search

The marvelous uniqueness of vibration responses of faulty roller bearings can be simply observed through its vibration signature. Therefore, vibration analysis has been claimed as an effective tool not only for primitive detection but also for subsequent analysis. The dynamic behavior of roller bearings has been investigated by systematic modeling of system and its validation under diverse operating conditions. This article presents an overview of imperative marks in the development of dynamic modeling of rolling-element bearing, which especially predicted vibration responses of damaged bearings. This study aims to address dimensional analysis; a new and imperative way to model the dynamic behavior of rolling-element bearings and their real-time performance in a rotor-bearing system. The findings are described with influential advantages over earlier research to pinpoint the intention behind its development. A literature summary is trailed by remarkable findings and future directions for research.

Rolling bearings accomplishes a smoother force transmission between relative components of high production volume systems. An impending fault may cause system malfunction and its maturation lead to a catastrophic failure of the system that increases the possibility of unscheduled maintenance or an expensive shutdown. These critical states demand a robust failure diagnosis scheme for bearings. The present paper demonstrates a novel way to develop a dynamic model for the rotor-bearing system using dimensional analysis (DA) considering significant geometric, operating, and thermal parameters of the system. The vibration responses of faulty spherical roller bearings are investigated under various operating conditions for validation of the developed model. Multivariable regression analysis is performed to expose the potential of the approach in the detection of the bearing failure. Results obtained unveil the simple and reliable nature of the dynamic modeling using DA.

An imminent bearing fault detection can reduce downtime or avoid the failure of rotating machinery. Modern condition monitoring demands simple but effective bearing failure diagnosis by integrating dynamic models with intelligence techniques. In view of this, an integrated approach of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Dimensional Analysis (DA) is demonstrated to diagnose the size of the bearing faults. Several experiments were performed with artificially damaged bearings at different operating conditions on the developed rotor-bearing test rig. With a 0.1 mm error band to fix minor bugs, a two-performance indicator evaluated the model accuracy. Comparison of the performance of DA and ANFIS models with experimental and Artificial Neural Network (ANN) validated the potential of the present approach. The results showed that the simplicity of the DA and the superior performance of the ANFIS contributes to detecting bearing fault effectively and accurately in the diagnosis of industrial machines.

A frictionless force transmission between the mechanical components of high production volume systems was accomplished by Rolling Element Bearings, which leads to being an imperative item of the condition-based maintenance (CBM). A prominent fault would cause malfunction, and its substantial growth may lead to catastrophic failure of such machinery ultimately give rise to unscheduled maintenance, and in extreme cases turns into a quite expensive shut down of the same. These decisive situations explore a growing demand for a robust failure diagnosis scheme for bearings. The present work demonstrates a novel approach to develop a dynamic model for vibration response of spherical roller bearings using dimensional analysis with Buckingham’s pi theorem (BPT) by considering significant geometric, operating and thermal parameters of the system. The results obtained have been validated with experiments performed on the developed test rig under diverse operating conditions. Vibration amplitudes considerably enhanced in the presence of rotating speed, applied load, bearing temperature, and volume of defect. Multivariable regression analysis is performed to reveal the effectiveness of the model for precise detection of impending bearing failure. The vibration response obtained by the DA model, experimental runs and MVRA are agreeing with physical perceptive and potentials. Results indicate the simplicity and reliability of the approach.