An Artificial Intelligence Model for the Construction of a Health Indicator for Gears

The purpose of condition monitoring is to monitor the conditions of an asset in order to predict its failure. The first step in implementing condition monitoring is to establish a health indicator. To construct the health indicator, operational data such as vibrational data should be collected from the asset during its operation, followed by extracting meaningful features from the data.This study introduces an artificial intelligence model (convolutional autoencoder) for feature extraction from the vibration data, which only requires healthy status data for training. For this purpose, the vibration data of the asset is gathered online during operation in a healthy state to establish the healthy dataset. Then, the deep learning model is trained by the healthy dataset. Finally, after the failure stage is detected, the health indicator is established by measuring the differences in the vibrations of the healthy and the failure conditions. The performance of the proposed model is evaluated by vibrational data of a gearbox. The health indicator exhibits a monotonically increasing degradation trend and has good performance in terms of detecting incipient faults.