Quality relevance and lifespan prediction utilizing data mining
Recommended technical proposal for customers considering the measurement of effects and quality improvement in predictive maintenance and equipment management! Utilizing large-scale data and machine learning as well!
This document explains, based on our company's implementation results and experience, the necessary considerations for threshold examination, which is always a concern when conducting predictive maintenance, as well as what indicators should be used when considering and implementing predictive maintenance. Additionally, we focus on the analysis of causal relationships with quality-related issues, which we have received many inquiries about in recent years, in conjunction with equipment maintenance. When building IoT and predictive maintenance systems, it is essential to start with a system that has a completion level of around 60 to 70 points, rather than aiming for a perfect score of 100 from the beginning, and to gradually improve the system towards the desired state. This document introduces some of the essence of that approach.
- Company：ウェーブフロント 本社
- Price：Other
