The deployment of local computing resources within or adjacent to an elevator installation to process sensor data, run predictive algorithms, and enable real-time decisions without relying solely on cloud connectivity.
Full Definition
Edge computing brings computational intelligence directly to the elevator controller or a dedicated edge gateway installed in the machine room. Instead of sending all sensor data to a cloud platform for analysis, an edge device processes data locally — enabling millisecond-response fault detection, predictive maintenance alerts, and local AI inference even during network outages. Key elevator edge applications include: vibration signature analysis for bearing wear detection; door cycle performance trending; motor current signature analysis (MCSA) for winding degradation; and energy consumption optimisation per trip. Edge devices complement rather than replace cloud analytics: aggregated and filtered data is periodically uploaded for fleet-wide benchmarking and digital twin synchronisation. Industrial-grade edge hardware (IEC 61131-based PLCs or ARM-based gateways) must operate within the machine room thermal range (+5 to +40°C per EN 81-20). Cybersecurity of edge devices is governed by IEC 62443. Edge computing reduces bandwidth costs, latency, and cloud dependency, making it increasingly standard in smart elevator systems.
Modern elevator installations with predictive maintenance programmes and remote monitoring — typically combined with IoT sensors and digital twin platforms
