Bearing Fault Diagnosis in Mechanical Gearbox, Based on Time and Frequency - Domain Parameters with MLP-ARD

Year 2014, Volume: 10 Issue: 2, 101 - 106, 01.04.2014

Dimitrios Katerıs Dimitrios Moshou Theodoros Gıalamas İoannis Gravalos Panagiotis Xyradakıs

Abstract

Gearboxes are one of the most important parts of the rotating machinery employed in

industries. Their function is to transfer torque and power from one shaft to another. If faults occur

in any component (bearings) of these machines during operating conditions, serious consequences

may occur. Consequently, condinuous monitoring of such subsystems could increase reliability of

machines carrying out field operations. Recently, research has been focused on the implementation

of vibration signals analysis for the health status diagnosis in gearboxes having as a base the use

of acceleration measurements. Informative features sensitive to specific bearing faults and fault

locations were constructed by using advanced signal processing enabling the accurate

discrimination of faults based on their location.

This work presents a fault diagnosis method for a mechanical gearbox with time and frequency -

domain features by using a Multilayer Perceptron with Bayesian Automatic Relevance (MLP-ARD)

Neural Network.

The time and frequency-domain vibration signals of normal and faulty bearings are processed for

feature extraction. These features from all the signals are used as input to the MLP-ARD. The

experimental results show that the proposed approach (MLP-ARD) presents very high accuracy in

different bearing fault detection. This approach will be extended as regards real-time fault

detection of rotating parts in agricultural vehicles where the anticipation of detection of incipient

failure can lead to reduced downtime.

Keywords

Gearbox, fault detection, neural network, bearing

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