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• Traditional elevator fault monitoring methods are less timely and inaccurate. • Flink performs better than Spark Streaming in terms of elevator data processing. • A finite state machine is used to monitor the operation of the elevator control system. Under "Industry 4.0", the implementation and application of big data and Artificial Intelligence (AI) technology in the elevator industry has become more and more common. With the surge of the elevator operation data and higher requirements for its real-time performance, the traditional elevator fault monitoring is inaccurate, which needs to be solved urgently. In this paper, a fault monitoring and diagnosis method of elevator based on AI and big data is proposed. Firstly, the elevator system and its fault types and causes are analyzed. Then in order to select the best big data processing tools, the performance of Flink and Spark Streaming is compared. The results show that Flink features faster computing speed and is more suitable for handling big data. Thirdly, a pattern recognition algorithm based on finite state machine (FSM) is proposed to monitor the running state for whole elevator control system. Finally, simulation experiment has been made. Graphical Abstract [Display omitted]. [ABSTRACT FROM AUTHOR]