Recently, the Coal Machinery Equipment Total Factor Data Cooperation Consortium, led by Luculent, has been successfully selected into the first national pilot list for the development of high-quality industrial datasets empowered by artificial intelligence.
As one of only 14 selected projects nationwide and 2 in Jiangsu Province, this national honor has drawn intensive coverage by industrial and information technology authorities at national, provincial and municipal levels, as well as major media including Xinhua Daily/Jiaohuidian, Jiangsu New Space, Nanjing Release, Nanjing Daily, Nanjing News and Gulou WeChat Bulletin. It has also been widely forwarded by industry associations and industrial chain partners, with the total online exposure exceeding 200,000.
The report from Jiaohuidian is excerpted as follows:
As industrial intelligent transformation accelerates, massive production data often remains dormant, failing to support the in-depth application of AI in workshops and coal mines.
“For AI to truly take root in industrial scenarios and solve practical problems, high-quality data integrated with in-depth industry expertise is indispensable,” said Wu Aibin, Chairman of Luculent. “The core objective of our consortium is to build a unified, secure, credible and value-circulable intelligent data base for the coal machinery equipment industry.”
This is exactly the original intention of the Ministry of Industry and Information Technology in launching the Industrial Data Foundation Initiative. This pilot program aims to select a group of capable pathfinders to take the lead in key fields and explore effective methods to collect accurate data, integrate comprehensive resources and realize high-value data application, so as to drive the solid implementation of industrial AI. As the cornerstone of national energy security, the coal machinery equipment industry faces an urgent demand for intelligent upgrading, making it one of the first pilot fields.
A large coal mining equipment boasts a service life of more than ten years, covering design and drawing, factory manufacturing, underground operation, daily maintenance and final scrapping and recycling. It involves multiple parties in R&D, production, operation and service, with a long and complex data chain.
“In the past, we accumulated massive equipment operation data, yet most were dormant raw materials that could not directly support intelligent decision-making,” Wu Aibin introduced. “Through this pilot project, we will join hands with industrial chain partners to convert scattered and heterogeneous raw data into high-standard data resources available for AI model application via unified standards and advanced tools. This will deliver accurate guidance on maintenance scheduling and process optimization, and fundamentally improve the reliability and intelligent level of coal machinery equipment.”
Specifically, the consortium will build a full-life-cycle digital archive for coal mining equipment from birth to decommissioning, tackle key technologies such as multi-source data collection, intelligent fault diagnosis and remaining useful life prediction, formulate unified data standards and industry knowledge corpus, and realize credible data sharing with the mechanism of raw data confined within domain, data usable but invisible.
For example, in the design phase of a new shearer, engineers can optimize design parameters by referring to massive historical operation data of similar equipment to enhance inherent equipment performance. On the production line, data of key processes including welding and heat treatment is collected and analyzed in real time. The system will issue early warnings against minor deviations to ensure all factory components deliver stable quality. In underground coal mines, subtle changes in equipment vibration and temperature are precisely monitored. Supported by intelligent model analysis, potential faults can be predicted in advance, transforming the maintenance mode from breakdown maintenance to predictive maintenance.
Leading such a national key project featuring multi-participant collaboration and complex technologies fully tests the comprehensive strength and practical experience of Luculent. As a national single-item manufacturing champion and an MIIT-certified Grade A cross-industry and cross-domain industrial internet platform enterprise, Luculent has deeply cultivated heavy industries such as energy and coal mining for over two decades. Its core competitiveness lies in profound understanding of data, scenarios and business demands, enabling the company to translate in-depth insights into industrial equipment and technological processes into professional software models and data processing capabilities. Up to now, Luculent has served a number of large energy groups including China National Coal Group, Shaanxi Coal and Huadian Coal Industry, with mature practices and technological achievements in intelligent equipment operation and maintenance and industrial big data. The company also claimed the championship of the National Industrial Internet Competition.
The consortium gathers national scientific research platforms, top universities, leading manufacturers and innovative technology enterprises, forming a closed-loop integrated ecosystem of industry, academia, research and application. It owns full-stack capabilities covering data collection, governance, labeling, model training and scenario implementation, ensuring all technologies originate from actual business needs and serve on-site production, avoiding disconnection between technology and practical demands.
In the view of Luculent, the long-term value of this project is to explore a set of methodologies, toolchains and standard systems for constructing high-quality datasets in complex equipment manufacturing through pilot practice. It will help enterprises activate data assets more efficiently and enable industrial AI to become a solid driving force for high-quality manufacturing development.
