10 results found. 1 - 10 of 10 results
Equipment maintenance DX by sensor data × data analysis
This article explains Hotelling's T-squared method, which is a representative data analysis method used in predictive maintenance and specialized for data science. It is a simple and powerful method of predictive maintenance data analysis that uses the normal distribution.
Equipment maintenance DX by sensor data × data analysis
This article explains Hotelling's T-squared method, which is a representative data analysis method used in predictive maintenance and specialized for data science. It is a simple and powerful method of predictive maintenance data analysis that uses the normal distribution.
Equipment maintenance DX by sensor data × data analysis
Predictive maintenance requires detecting signs of equipment deterioration that appear in sensor data, and a key to achieving this is to select the appropriate data analysis methods. This article focuses on data analysis for predictive maintenance, and introduces frequently used data analysis methods and the process of selecting them.
Equipment maintenance DX by sensor data × data analysis
Predictive Maintenance (PdM) is a form of maintenance that is expected to contribute to sophistication and labor-saving of maintenance. This article provides an overview of the definition of PdM and the technologies used in it, and introduces the key points to be aware of prior to implementation.
Predictive maintenance is considered the ideal form of equipment maintenance, which has evolved from breakdown maintenance to preventive maintenance. However, it is also true that there is currently no established method that realizes predictive maintenance. Accordingly, we will now explain how to utilize sensor fusion, AI, and edge AI to provide hints to realizing predictive maintenance based on basic knowledge of equipment maintenance.
Smart Factories: Giving Form to Digital Transformation in the Manufacturing Industry
As explored in this article, maintenance operations are a gateway to building smart factories. Predictive maintenance is taking off. By detecting symptoms of imminent equipment failure on production lines from slight vibration or other changes, manufacturers can take action before it's too late. This thinking can now be extended to proactive yield and quality management.
IoT is the key to breaking free from the dependence of maintenance work on intuition and unique skills. In Part 2, we will hear in detail from Kenichi Furutani, a consultant at Japan Excel-Management Consulting (JEMCO), about the specific methods and keys to implementing IoT for maintenance work.
Standardization of maintenance work is one of the most important factors in improving the competitiveness and productivity of factories. We asked Kenichi Furutani, a consultant at Japan Excel-Management Consulting (JEMCO), about how to break free from dependence on intuition and unique skills for IoT-based maintenance work.
Smart Factory Case Study
In Part 1, we learned all about the IoT integration that took place at Fukui Murata Manufacturing, as well as the predictive maintenance system and achievements that the integration made possible. In Part 2, we look into how they proceeded with IoT integration at their manufacturing site.
Smart Factory Case Study
Since the popularization of the word “IoT,” many have recognized the necessity of its implementation in various industrial fields. This interview is a discussion presenting the points to advancing the integration of IoT technology in manufacturing sites in addition to the results and background of the implementation of IoT technology at Fukui Murata Manufacturing Co.
