A review on machinery diagnostics and prognostics implementing condition-based maintenance

Artificial intelligence for fault diagnosis of rotating machinery: A review

Vibration of cracked structures: A state of the art review

Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods

The problems of stiffness and damping characteristics of isothermal elastohydrodynamic mixed lubricated point contact are evaluated numerically considering surface roughness effect including asperity contact load. A set of equations under steady-state and dynamic conditions is derived from the classical Reynolds equation, using linear perturbation method. The elasticity equation and steady-state Reynolds equation are solved simultaneously for finding the steady-state pressure distribution, using finite difference method. Then, the set of perturbed equations is solved for the dynamic pressure distribution in the contact. A Gaussian surface roughness is adopted to model both surface roughness and mixed lubrication. Total load capacity of the contact is calculated from the lubricant film pressure and contact pressure distribution. Results are compared with those of smooth isothermal cases. The stiffness and damping coefficients of the contact are determined using the dynamic pressures. The asperity c...

On the Dynamics of Elastohydrodynamic Mixed Lubricated Ball Bearings. Part I: Formulation of Stiffness and Damping Coefficients:

Instability of asymmetric shaft system

Rotating machinery is common in any industry. Rotating machinery in the modern era are designed for higher running speeds, tighter clearances and working under extreme conditions enhancing efficiency of the system to produce and transmit more power. All these lead to many rotordynamic challenges. Main cause of vibrations is faults in the rotating systems like unbalance, looseness, etc. In this paper a method is proposed to identify unbalance and looseness in rotor bearing system using artificial neural networks (ANN) by two different methods; one is by statistical features and the second by amplitude in frequency domain. In the first case statistical features are used to train and test the ANN, and in the second case amplitude in frequency domain is used to train and test the ANN. Experiments are conducted on the rotor bearing system running at 40 Hz and vibration data is collected by simulating different unbalance conditions in the rotor. And also experiments are conducted by creating looseness in the system by loosening the pedestal bolt. Various statistical features and amplitudes in frequency domain are extracted separately from this vibration data and are fed to neural network. It is observed that statistical features are giving good results over frequency domain amplitudes. ANNs are used to identify the unbalance severity and looseness. These results are useful for making maintenance decision.

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Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems

Synchronous lateral vibration is a frequent cause of machine failure and is probably the most common source of machine noise and vibration. Among the broad ranges of faults that cause synchronous v...

Balancing of flexible rotor with bow using transfer matrix method

The dynamics and diagnostics of rotor with faults have been gaining importance in recent years. In the present study a model-based method is proposed for the on-line identification of faults in a rotor. The fault-induced change of the rotor system is taken into account by equivalent loads in the mathematical model. The equivalent loads are virtual forces and moments acting on the linear undamaged system to generate a dynamic behaviour identical to the measured one of the damaged system. The rotor has been modelled using FEM. Each fault has been identified for its size and location on the shaft for transient and steady rotors. The nature and symptoms of the fault are ascertained using FFT or Wavelet transforms. For on-line rotor fault identification a combined (hybrid) model and signal-based approach is suggested.

A. S. Sekhar

Papers

On the Dynamics of Elastohydrodynamic Mixed Lubricated Ball Bearings. Part I: Formulation of Stiffness and Damping Coefficients:

Instability of asymmetric shaft system

Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems

Balancing of flexible rotor with bow using transfer matrix method

On-Line Rotor Fault Identification