Mitsubishi Electric Develops Physics-Embedded AI for Accurate Estimation of Equipment Degradation from Small Amounts of Training Data

Reduces maintenance costs while supporting productivity and quality at manufacturing sites

TOKYO--(BUSINESS WIRE)--Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed a physics-embedded AI that can accurately estimate equipment degradation using minimal training data. The technology is an outcome of the company’s Neuro-Physical AI initiative within its Maisart^® AI program, which emphasizes reliability and safety in the physical world. Leveraging Mitsubishi Electric’s extensive equipment-development expertise, the new technology supports the optimization of manufacturing-site assets to maintain productivity and quality as well as reduce maintenance costs.

Japan’s manufacturing sector is deploying increasingly sophisticated production equipment at a time when the nation’s aging and shrinking population is reducing the number of experienced maintenance technicians. Meanwhile, there is a growing demand for preventive-maintenance solutions that can predict equipment degradation for timely responses, since continued use of degraded equipment can result in equipment failure or defective products. Conventional preventive maintenance typically mimics equipment behavior using mathematical models or simulations to estimate degradation. But this approach requires domain experts with knowledge of physical systems to design degradation-detection mechanisms from scratch, which can require significant time and effort. To address this issue, there is a movement to estimate degradation by training AI with operational data. However, these efforts typically require vast amounts of data to comprehensively cover diverse operating patterns, unit-to-unit variability and installation environments, as well as retraining whenever conditions change, hindering the practical deployment of AI to estimate equipment degradation.

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