TASCNet: A Lightweight Bearing Fault Diagnosis Network under Unknown Working Conditions Enabling Industrial Internet of Things

Accurately identifying the health condition of the wheel bearings is of great significance in ensuring the safety of freight trains. However, due to the limited computing resources, it is required to develop a lightweight fault diagnosis model for edge computing scenarios. In practice, since the operating conditions (i.e., loads and speeds) are varying, there is a demand for an effective diagnostic model with limited data. To address these issues, this paper proposes a novel fault diagnosis method based on compressed time-frequency matrix (CTFM) and a triplet attention self-calibrated convolution network (TASCNet), enabling Industrial Internet of Things (IIoT). First, the measurement obtained from the IIoT platform is processed by the proposed CTFM, which transforms the signal into the time-frequency domain with a tailored size. Moreover, a new model, TASCNet, is established to extract tiny changes while expanding the receptive field. On the one hand, it effectively captures cross-dimensional interaction between channel and spatial dimensions of the input. On the other hand, it constructs long-range spatial and inter-channel dependencies around each spatial location. Finally, the proposed method is evaluated by a dataset collected from an industrial railway wheelset bearing test rig. Furthermore, its performance has been compared with that of four conventional fault diagnosis methods. The results demonstrate that the proposed method outperforms other methods in terms of diagnostic accuracy, robustness, and computational cost.