Unbalance Failure Recognition Using Recurrent Neural Network

Many machine learning models have been created in recent years, which focus on recognising bearings and gearboxes with less attention on detecting unbalance issues. Unbalance is a fundamental issue that frequently occurs in deteriorating machinery, which requires checking prior to significant faults such as bearing and gearbox failures. Unbalance will propagate unless correction happens, causing damage to neighbouring components, such as bearings and mechanical seals. Because recurrent neural networks are well-known for their performance with sequential data, in this study, RNN is proposed to be developed using only two statistical moments known as the crest factor and kurtosis, with the goal of producing an RNN capable of producing better unbalanced fault predictions than existing machine learning models. The results reveal that RNN prediction efficacies are dependent on how the input data is prepared, with separate datasets of unbalanced data producing more accurate predictions than bulk datasets and combined datasets. This study shows that if the dataset is prepared in a specific way, RNN has a stronger prediction capability, and a future study will explore a new parameter to be fused along with present statistical moments to increase RNN's prediction capability.