Research on IoT perception mechanism of elevator fault warning

This study proposes a fault prediction mechanism for elevator systems with the help of the Internet of Things. A multi-layer architecture system integrating sensing nodes, edge processing and cloud decision-making is designed. Multimodal sensors are configured at key components of the elevator to collect real-time data, and anomaly detection algorithms are used to process data locally, relying on random forest classifiers for global evaluation. A method that can dynamically set thresholds is adopted to fit different operating conditions. The experimental results of the real data set in 2023 show that the system achieves an average fault detection accuracy of 95.4%, and the warning response time is compressed to less than 3 minutes. Compared with the traditional monitoring method that relies on patrol inspections, the system greatly enhances the real-time response level, reduces the frequency of false alarms, and enhances the reliability of operation. This framework provides a scalable and robust solution for predictive maintenance of vertical transportation systems, and also provides a reference style for similar applications in other safety-critical fields.