Unbalance Rotor Parameters Detection Based on Artificial Neural Network: Development of Test Rig

Gohari, Mohammad, Kord, Ahmad and Jalali, Hassan (2022) Unbalance Rotor Parameters Detection Based on Artificial Neural Network: Development of Test Rig. Journal of Vibration Engineering & Technologies, 10 (8). pp. 3147-3155. ISSN 2523-3920

last revised Unbalance Rotor Parameters Detection Based on ANN2022.pdf - Accepted Version

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Official URL: https://doi.org/10.1007/s42417-022-00546-4


Condition monitoring techniques provide vital data for operators to avoid unpredicted and unwanted stops of machines caused by faults. One of these techniques is vibration analysis, which is used for faults diagnosis and prognosis such as shaft bending, misalignment, lousy bearing, worn gears, unbalances of rotors, etc. Moreover, vibration signals can be employed in intelligent algorithms like Fuzzy Models, Support Vector Machines, and Neural Networks to prepare better and more accurate predictions of current and future conditions of the machine. This paper discusses the application of vibration signals in the prediction of rotor unbalance parameters including the unbalance location and amount. Some statistical features were applied to the inputs of the neural network that had been derived from the time and frequency domains of bearing acceleration signals. The experimental study shows that the developed model can estimate these parameters with acceptable accuracy.

Item Type: Article
Uncontrolled Keywords: Rotary test rig, Unbalance amount, Location of unbalance, Eccentric mass, Neural network model
Subjects: H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 30 Aug 2022 10:34
Last Modified: 26 Jun 2023 03:30
URI: https://nrl.northumbria.ac.uk/id/eprint/49977

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