The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

Ceramics are commonly used as high-temperature structural materials which are easy to fracture because of the propagation of thermal shock cracks. Characterizing and controlling crack propagation are significant for the improvement of the thermal shock resistance of ceramics. However, observing crack morphology, based on macro and SEM images, costs much time and potentially includes subjective factors. In addition, complex cracks cannot be counted and will be simplified or omitted. Fractals are suitable to describe complex and inhomogeneous structures, and the multifractal spectrum describes this complexity and heterogeneity in more detail. This paper proposes a crack characterization method based on the multifractal spectrum. After thermal shocks, the multifractal spectrum of alumina ceramics was obtained, and the crack fractal features were extracted. Then, a deep learning method was employed to extract features and automatically classify ceramic crack materials with different strengths, with a recognition accuracy of 87.5%.