Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis.

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis.

Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction.

As one of the most important components of machinery, once the bearing has a failure, serious catastrophe may happen. Hence, for avoiding the catastrophe, it is valuable to predict the remaining useful life (RUL) of bearing. Health indicators (HIs) construction plays a greatly important role in the data-driven RUL prediction.