As modern production technology progresses, equipment complexity escalates, resulting in a rising failure rate. An intelligent remote monitoring and diagnosis system for equipment based on cloud-edge collaboration technology is designed to address the problems of poor data transmission ability and low fault diagnosis accuracy in traditional monitoring and diagnosis systems. The system realizes information perception, collection, and edge computing through edge intelligent devices. The feature extraction, pattern recognition, and intelligent information diagnosis are realized in the cloud platform, and the equipment's running state and diagnosis results are displayed in real time at the terminal. The research and design of this system are of great significance in reducing unexpected downtime of equipment and improving production efficiency. © Published under licence by IOP Publishing Ltd.

During the operation of the vibrating screen, failures will inevitably occur, affecting production efficiency. This paper designs an intelligent monitoring system for the vibrating screen. It collects the vibrating screen's vibration data by using six-axis sensors at different measuring points, analyzes the signals collected by the sensors to extract features, accumulate historical data and upload it to the cloud server. Through the cloud's big data and expert system, the operation status of the vibrating screen will be evaluated and predicted. If the vibrating screen fails, it will promptly alarm and shut down. The research and promotion of this system is of great significance to the construction of smart factories and the improvement of the industrial management level. © 2020 IEEE.

Rolling machine is an important mechanical equipment in the building materials industry. Its universal joints are prone to failures, and timely fault judgments are made to predict the occurrence of failures and perform effective maintenance, which is of great significance to the safety of enterprises. Among them, the universal joint coupling often occurs due to unbalanced transmission shaft, loose bolts, loose transmission shaft, and cross shaft damage. Rolling machine failures usually cause abnormal vibrations. By measuring the vibration response and extracting effective features, the operating status and deterioration trend of the equipment can be predicted in time, the problem take can be solved in time before failure to prevent greater losses. This paper studies the monitoring, diagnosis and analysis methods for the alignment faults of the universal joint of the roller press, analyzes the causes, mechanisms and phenomena of the alignment faults of the roller press, studies the extraction methods of the fault features, analyzes the fault diagnosis model and failure trend prediction method, and built a monitoring system for experimental verification. Experiments show that the designed data acquisition method can capture the vibration signal in time, and the analysis model can also analyze and identify the fault type based on the existing fault knowledge and rules. © 2020 IEEE.