Surajkumar Goraksha Kumbhar

The present study focuses on the numerical modeling of the thermal buckling behavior of tapered laminated composite beams validated by experimentation. Lagrange's energy‐based governing differential expression was derived utilizing classical laminated plate theory and higher‐order shear deformation theory to analyze the tapered composite beam's thermal buckling property. The uniform temperature rise environment is employed to estimate the critical buckling temperature of the tapered laminated composite beam. The obtained results from the numerical approaches were validated with the experimental results and the previous results obtained from the literature associated with the fundamental frequencies of the structure and critical buckling temperature. The parametric study was incorporated in this present work to investigate the influence of the structural parameters and the geometrical parameters like taper angle, ply orientation, aspect ratio, and boundary constraints of the beam.

Condition monitoring of rotor dynamic is recognized as an advanced preventative maintenance technique for fault-free operation. Faulty bearings in rotating machines may cause severe problems and even untimely breakdowns. This work demonstrates the power of the finite element analysis (FEA) model and dimension analysis technique (DAT) to analyze the effect of the depth and slope angle of surface faults on the bearing contact characteristic. Experimentation is performed to investigate the vibration characteristics of ball bearings. The FEA, DAT, and experimentation show that vibration amplitude is a vital function of surface fault size. The current approach of FEA with DAT reflects their reliability and accuracy for the diagnosis of rotor systems. The present method was found effective in predicting vibration amplitude and defect frequency within acceptable error.

The vibration level is a function of the defects in the bearing. By identifying a change in vibration level, one can predict the dynamic behavior and fault in the rotor-bearing system. An imminent bearing fault detection can reduce downtime or avoid the failure of rotating machinery. The condition monitoring or maintenance schedule can be set well if the diagnosis estimate bearing fault size accurately. In view of this, the adaptive neurofuzzy inference system (ANFIS) and dimension analysis (DA) were utilized to detect the bearing fault size. Several experiments were performed at different rotating speeds on the rotor-bearing system. Localized defects were simulated on bearing races artificially using electrode discharge machining (EDM) and the vibration responses are acquainted by accelerometer and fast Fourier techniques (FFT). With a 0.1 mm error band to fix minor bugs, a two-performance indicator evaluated the model accuracy. A comparison of the performance of models with experimental results and artificial neural network (ANN) validated the potential of the present approach. The results showed that an ANFIS performs better over DA and ANN. This contributes to detecting bearing fault effectively and accuracy improvement in the estimation of the bearing fault size.

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.