Intelligent condition monitoring of gear system at variable load and variable speed using vibration data

Gears are integral components of industrial machines that are used for converting power and motion in different industrial processes. Effective monitoring of gear health and early detection of their faults is essential. It assists in averting sudden shutdowns and, in addition, reduces the costs of maintenance while at the same time increasing the efficiency of the production process. This paper proposes an approach for gear fault prognostics and condition monitoring under variable speed and flexible load conditions using vibration data. The approach involves the utilization of Artificial Neural Network (ANN) in combination with signal processing techniques to analyze the gear vibrations in induction motors (IMs) with the aim of establishing the efficiency of the gear system in different conditions of variable loads and speeds. This real-time data is recorded from the two sensors that are fixed on the exterior of the induction motor horizontally and vertically. In addition, this methodology consists of data collection, data cleaning, and data preprocessing, enhancing the ability of deep learning models, leading to accurate predictions and classification of gear faults. The models are tested and evaluated for different evaluation metrics and prove to be very effective in monitoring the overall health of gears and detecting their faults. The variable load and variable speed models were able to achieve accuracies of 96.44% and 93.82% respectively. The precision, recall, and F1 score are 0.97, 0.96, and 0.96 for the variable load model, while 0.96, 0.94, and 0.94 for the variable speed models.