Incipient fault detection of planetary gearbox under steady and varying condition

As an important component in rotating machines, gearbox failure will lead to costly economic losses. Generally, incipient fault features of gearbox are weak and concealed in a set of time-varying vibration signals that are challenging to identify effectively. Based on that, a new method is proposed for incipient fault detection(FD) under steady and variable conditions of gearboxes based on improved octave convolution in this paper. First, the vibration signals are transferred into images via the symmetrized dot pattern(SDP) method. Then, the proposed method enhances image detail learning by adding convolution kernels and introducing residual connections in the high-frequency component of the octave convolution. Meanwhile, self-attention units are introduced in the information interaction branches. After that, the improved octave convolution is applied to the ResNet50 backbone network(IOC-ResNet50) to mine deep fault features. Compared with other published methods, the results indicate that the proposed performs superiorly in both time-varying conditions and steady state.