Aging of many facilities in factories, plants, and buildings and a chronic shortage of labor for their maintenance have been problems. Thus, there is demand for services that require less labor (such as remote maintenance), with a forecast domestic market size of 2.935 trillion yen in 2025 (*1).
To maintain and inspect aging facilities at high accuracy, good knowledge of those facilities with understanding of their history of maintenance and inspection are crucial. However, a problem at present is that specialized data such as maintenance and inspection reports, in which experts’ experiences and knowledge were accumulated, are not fully organized for utilization.
Large general-purpose language models are known as a fundamental technology for understanding documents with high accuracy. Those models are trained using a vast amount of document database (*2) comprising general documents in order for them to understand the context of sentences by unsupervised learning, and they are attracting attention as a versatile technology applicable to various services, such as Q&A and machine translation. However, the computational scale of general-purpose language models is large, and ensuring adequate computational resources is difficult in actual infrastructure maintenance, creating a challenge. Also, when using general-purpose language models for specific business sectors, technical documents particular to the field need to be additionally learned. This means that large sets of sector-specific data are needed, requiring further computational resources.
If specialized data can be understood with high accuracy using limited computational resources, then it would be possible to greatly improve the efficiency of maintenance and inspection work, including automatic inspection of incomplete reports, rapid formulation of countermeasures by referring to similar past incidents, and replacement of machine components before failures occur based on the trends from failure analysis.