Using artificial intelligence for data reduction in mechanical engineering

TL;DR

This paper introduces AI-based models using neural networks and support vector machines to significantly reduce data requirements for estimating gear vibration signals, achieving up to 75% data reduction.

Contribution

The paper proposes two novel AI models for data reduction in gear vibration analysis, demonstrating substantial efficiency improvements over traditional methods.

Findings

Data reduction of up to 75% achieved

Models validated on accelerated gear test data

Significant potential for efficient vibration monitoring

Abstract

In this paper artificial neural networks and support vector machines are used to reduce the amount of vibration data that is required to estimate the Time Domain Average of a gear vibration signal. Two models for estimating the time domain average of a gear vibration signal are proposed. The models are tested on data from an accelerated gear life test rig. Experimental results indicate that the required data for calculating the Time Domain Average of a gear vibration signal can be reduced by up to 75% when the proposed models are implemented.