Triboelectric nanogenerator-assisted-gear fault diagnosis using multiplex temporally tuned singular value domain graphs and graph convolutional networks

Gears are major part of any rotating machinery. Gear fault diagnosis plays a crucial role in ensuring the reliable operation of rotating machinery. Vibration is the key parameter for gear fault diagnosis because gear faults produce repeated transient impulses in vibration signals. In this study, triboelectric nanogenerator (TENG) is utilized to capture these temporal vibration patterns under different gear conditions. Subsequently to diagnose the TENG fault data, this research introduces a new feature called as multiplex temporally-tuned singular value domain-graphs (MPlex-TSVG) that is derived using multiplex network theory and singular value decomposition (SVD) technique. The MPlex-TSVG first transforms the raw TENG signal into sequences of singular values extracted from localized time-scale segments using the SVD. Afterwards, the singular value-sequences are transformed into a network of graphs using multiplex graph approach that evaluates the inter-layer and intra-layer visibility between the distinct singular-value sequences. The MPlex-TSVGs thus obtained are classified via a graph convolutional network (GCN) model for distinguishing gear faults. This proposed approach is experimentally evaluated under two speed - and four dissimilar fault conditions on two different test setups and achieved high classification accuracies of 100% and 98.68% on the first setup dataset and 100% and 99% on the second setup dataset, respectively. In addition, it outperformed the existing methods using time-frequency images and convolutional neural network (CNN).