Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

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.

Genetically Engineered Phage-Templated MnO2 Nanowires: Synthesis and Their Application in Electrochemical Glucose Biosensor Operated at Neutral pH Condition.

To conveniently obtain one-dimensional MnO2 nanowires (NWs) with controlled structure and unique properties for electron transfer, the genetically engineered M13 phages were used as templates for precise nucleation and growth of MnO2 crystals in filamentous phage scaffolds, via the spontaneous oxidation of Mn(2+) in alkaline solution. It was found that the morphology of NWs could be tailored by the surface charge of M13 mutants. MnO2 crystals were uniformly distributed on the surface of negatively charged tetraglutamate-fused phage (M13-E4), significantly different from irregular MnO2 agglomeration on the weakly negatively charged wild-type phage and positively charged tetraarginine-fused phage. The as-synthesized M13-E4@MnO2 NWs could catalyze the electro-oxidation of H2O2 at neutral pH. To demonstrate the superiority of the electrocatalytic activity in the solution containing plenty of chloride ions at neutral pH, both glucose oxidase and as-prepared MnO2 NWs were used for fabricating the glucose biosensor. The proposed biosensor showed a wide linear range (5 µM to 2 mM glucose), a low limit of detection of 1.8 µM glucose (S/N = 3), good interassay and intra-assay reproducibility and satisfactory storage stability. Due to the superiorities of synthesis and electrochemical performance, the as-prepared MnO2 NWs are promising for applications in electrocatalysis, electrochemical sensor, and supercapacitor.