On March 17, Tianjin Port successfully made a breakthrough in equipment status traceability with its self-developed technology project titled “Research and Application of Self-service Analytics Model for Bulk Cargo Port Loading and Unloading Production Business”. With the reading algorithm through self-developed time series data, the project effectively addressed the challenge of precision recognition for the idling time of belt conveyors within a second level, successfully enabling automatic traceability and analysis of conditions including operation, downtime, faults, and idling for all handling equipment across the entire site.
Previously, the lack of effective equipment status traceability methods required equipment status analysis to rely on manual assessments based on loading and unloading production records and PLC tag status, leading to issues related to the accuracy, timeliness, and depth of data that could no longer meet the increasing demands of equipment management and energy conservation. In response to the urgent need for refined equipment analysis, Tianjin Port established a dedicated team to deeply integrate PLC data with the production management system, collecting real-time key operational parameters. The development of the equipment status traceability algorithm now allows for whole-process tracking and AI analysis of operation, downtime, faults, and idling of all loading and unloading equipment across the entire site within the Company, as well as the capability to replay equipment status over any time, to accurately identify abnormal instances, including idling and inefficient operation, providing solid data support for future applications of artificial intelligence in predicting potential equipment failures and developing quantitative models for idling energy consumption of belt conveyors.
In pursuing dual carbon goals, Tianjin Port will continue to focus on equipment status data and explore the implementation of intelligent analytical predictions for equipment status by introducing AI analysis technology, to accurately identify issues including idling and inefficient operations, while integrating energy consumption data to develop scientific energy-conserving strategies, ultimately aiming at reducing operation costs for the Company. This commitment to sci-tech innovation will accumulate strength for the Company’s high-quality development of “secondary entrepreneurship”.
