Quality relevance and lifespan prediction using data mining.
Recommended technical proposal for customers considering the measurement of the effectiveness of predictive maintenance and equipment management operations, as well as quality improvement! 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 that inevitably arise 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 or 70 points, rather than aiming for a perfect 100-point system from the outset, 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
