An automatic speed adaption neural network model for planetary gearbox fault diagnosis

In real-world applications, fault detection and diagnosis of planetary gearboxes are vital if it can be employed to avert catastrophic failure consequences in rotating machinery. Fault diagnosis usually starts with collecting vibration signals from rotating machinery. These vibration signals are usually produced in non-stationary operating conditions with time-varying loads and speeds, which makes fault diagnosis more challenging. Signal processing methods are typically selected for fault diagnosis to capture either time, frequency, or time-frequency based diagnostic features from measured vibration signals. Yet, it is usually a costly or timeconsuming process and, sometimes, heavily dependent on human expertise. Although current deep learning algorithms offer an efficient and intelligent diagnostic strategy for fault diagnosis, unfortunately, most of the reported algorithms are basically only valid for the stationary operating conditions. To address the challenges of non-stationary operating conditions, in this paper, an Automatic Speed Adaption Neural Network (ASANN) model within the incorporation of instantaneous rotating speed is proposed, and it provides an end-to-end learning fashion with the guidance of rotating speed information. With the incorporating of instantaneous rotating speed information, the proposed ASANN model enables the extraordinary capacity for planetary gearbox fault detection under varying operational scenarios. The validity of the ASANN model is verified by an experimental investigation of fault diagnosis in a planetary gearbox.