Elevator operation and safety is taken for granted until an “Under maintenance” sign is displayed on its doors. These machines are a critical component of many lives to transport people and items from floor to floor within buildings. As with any mechanical and electrical machine, especially one with as many use cycles as an elevator, regular maintenance is important for continued operation. Elevator maintenance is typically performed under fixed time intervals, termed a preventative maintenance schedule. But this doesn’t fully utilize component lifetimes and is prone to a lack of maintenance if the elevator has higher average traffic than expected. A solution for these issues is to implement a predictive maintenance system (PdMS) onto the elevator. This study aims to develop such a system that consists of a sensor network, a microprocessor, and firmware to estimate whether the elevator’s operation is nominal and the remaining useful life (RUL) of critical components. A physical testbench has also been developed which consists of a motor, and an in-house dynamometer to confirm motor health and power output.
During the development of this system, an investigation into critical aspects of an elevator system was conducted, and critical system data was selected for unobtrusive measurement. Unobtrusive data acquisition is required such that the elevator’s operation is not disturbed, and for the PdMS to be compatible with any elevator. A large focus was placed on inductive motor RUL estimation which calculates motor efficiency, health, and records performance trends. The resulting system can log elevator use data, provides critical maintenance predictions, and can be installed onto elevators and other systems that would benefit from its RUL estimations.