Faced with the shortage of workers in the manufacturing industry and the long-term reliance on the experience of senior technicians for equipment maintenance, generative AI provides another solution. (downloaded from pexels)
[Reporter Qiu Qiaozhen/Reported from Taipei] Faced with the shortage of workers in the manufacturing industry and the long-term reliance on the experience of senior technicians for equipment maintenance, the Ministry of Economic Affairs has joined hands with the Digital Transformation Institute (Digital Transformation Institute) of the Institute of Information Technology and Youdema Technology to introduce generative AI into traditional Textile equipment has created "generative AI knitting machine health analysis and management" technology. Through IoT data collection, AI predictive maintenance, pure ground-side generative AI and multi-field management, equipment maintenance can shift from "repairing faults" in the past to early warning.
Compared with the past, when an abnormality occurs in factory equipment, maintenance personnel will inspect and determine the problem. This system collects knitting machine operation data and uses AI to analyze the equipment status and provide warnings before parts may be damaged, allowing on-site personnel to arrange maintenance in advance.
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In addition to predicting equipment abnormalities, the system can also further predict the life of parts, reducing the waste of resources caused by excessive replacement of parts. At the same time, it can identify abnormal equipment with high energy consumption early to reduce unexpected shutdowns and energy consumption.
For traditional manufacturing industries, equipment maintenance has long relied on the on-site experience accumulated by senior technical personnel. In the face of labor shortages and insufficient technical manpower, how to reduce the high dependence of equipment management on the experience of specific personnel has also become an important issue in smart manufacturing.
After the introduction of generative AI, on-site personnel can grasp the status of the machine more intuitively. Operators do not need to learn complex system interfaces, but can directly query equipment and factory information through daily conversations.
Considering the manufacturing industry's demand for information security of production data and equipment, this generative AI does not send data to an external cloud for processing, but is built directly inside the factory. Production and equipment data do not need to be uploaded to an external cloud. Enterprise data security is taken into account while introducing AI.
If an enterprise has different production bases, it can also use the same system to grasp the equipment status of each factory area, and at the same time manage the data in different areas separately to improve the efficiency of cross-factory management.
The Institute's team pointed out that in the face of challenges such as labor shortages, equipment maintenance and net-zero transformation in the manufacturing industry, the value of AI is not only to collect more data, but more importantly, to transform data into information that can be understood, judged and acted upon on site. By combining equipment health analysis and predictive maintenance through generative AI, we hope to lower the threshold for traditional industries to introduce smart manufacturing.
From the perspective of industrial application, this technology is not limited to knitting machines. The "Generative AI Knitting Machine Health Analysis and Management" technology adopts a modular software and hardware architecture and can be further extended to dyeing and finishing machines, setting machines and other textile equipment in the future. It also has the potential to be extended to machine tool tool life management and other machinery manufacturing fields.
The Information Policy Council stated that in the future, related technologies can also be expanded to overseas markets such as Southeast Asia with equipment, and it will continue to promote AI technology to enter more manufacturing sites to respond to corporate needs such as labor shortages, equipment maintenance, and energy management.
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