Fast Fourier Transform

A summary of fault modelling and predictive health monitoring of rolling element bearings

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump

Lubricating oil conditioning sensors for online machine health monitoring – A review

Simulation and analysis of vibration signals generated by rolling element bearing with defects

Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force

It is possible to model smart structures with piezoelectric materials using the product ANSYS/Multiphysics. In this study, the integration of control actions into the ANSYS solution is realized. First, the procedure is tested on the active vibration control problem with a two-degrees of freedom system. The analytical results obtained by the Laplace transform method and by ANSYS are compared. Then, the smart structures are studied by ANSYS. The input reference value is taken as zero in the closed loop vibration control. The instantaneous value of the strain at the sensor location at a time step is subtracted from zero to find the error signal value. The error value is multiplied by the control gain to calculate the voltage value which is used as the input to the actuator nodes. The process is continued with the selected time step until the steady-state value is approximately reached. The results are obtained for the structures analyzed in other studies. The active vibration control of a circular disc is also studied.

https://iopscience.iop.org/article/10.1088/0964-1726/13/4/003/pdf

Analysis of active vibration control in smart structures by ANSYS

In this study, a two-link manipulator with flexible members is considered. The end point vibration signals are simulated by developing a MatLAB code based on the finite element theory and Newmark solution. Experimental results are also presented and compared with simulation results. The mass and stiffness matrices are time dependent because the angular positions of the links change during the motion. Trapezoidal velocity profiles for the actuating motors are used. The time dependent inertia forces are calculated by using the rigid body dynamics. The inertia forces are due to the motors, end point payload mass and distributed masses of the links. The acceleration, constant velocity and deceleration time intervals of the trapezoidal velocity profile are selected by considering the lowest natural frequency of the manipulator structure at the stopping position. Various starting and stopping positions are considered. The root mean square (RMS) acceleration values of the vibration signals after stopping are cal...

Hira Karagülle

Papers

Simulation and analysis of vibration signals generated by rolling element bearing with defects

Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force

Analysis of active vibration control in smart structures by ANSYS

Vibration control of a two-link flexible manipulator:

Vibration control of a single-link flexible composite manipulator