Condition Monitoring of Industrial Elevators Based on Machine Learning Models

With the increasing demands for safe, robust and high efficient systems, the performance indicators should be quantified over the system’s life-cycle. Condition monitoring
drastically reduce the maintenance costs, prevents unscheduled working interruption of the system, keeps productivity performance and the system in safe operating mode. Artificial intelligence and machine learning models are able to process big data sets, harness the data and predict the failures. This paper presents a method to monitor the states conditions and to identify the faults that may appear during the operation of an industrial elevator by developing a Long Short-Term Memory model.