Since the2016years10Starting from this month and lasting for three months, our company successfully implemented the "Full Life Cycle Reliability Management of Visualized Intelligent Equipment" project at Qingdao Energy Thermal Power Co., LTD. The main contents of this project are:

(1Through overall equipment inspection and diagnosis, faults and potential hazards existing in the equipment are identified, and reasonable improvement plans are formulated to achieve the improvement of all equipment operation in accordance with various standards and regulations.

(2Provide high-precision, stable, reliable and user-friendly instruments and equipment, and conduct all-round and multi-technical means inspection, monitoring and analysis of the equipment.

(3Train the reliability management personnel of equipment to be proficient in the operation of both software and hardware.

(4Establish a group-level intelligent full-life-cycle equipment reliability management system and deploy and build a remote platform.

Through the implementation of this project, the unified management of the main equipment and important auxiliary main equipment of the power plant has been achieved. It has reduced emergency repair work, eliminated abnormal shutdown accidents, comprehensively enhanced the reliability and safety of equipment, and significantly lowered the operation and maintenance costs of equipment. Through the implementation of this project, problems that often occur in the auxiliary equipment of power plants, such as dynamic imbalance of motors and blades, loose rotors, broken rotor bars, stator eccentricity, misalignment and wear of couplings, unstable foundations, and loose mechanical connection parts, have been solved. The intelligent auxiliary equipment for various types of rotating equipment in power plants has been comprehensively monitored and diagnosed. Through real-time monitoring of equipment vibration conditions, spectrum analysis, automatic fault diagnosis, historical trend analysis and other means, the health status and reliability of the equipment can be understood at any time. With a large amount of status data as support, the accuracy of the diagnosis system has been greatly improved, and the timeliness of fault diagnosis has been enhanced And provide an accurate and reliable data foundation for the future big data platform.