Finite state machine elevator4/6/2024 The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform.
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