Do you have any of these issues?

This seminar focuses on restructuring Excel vibration data used in engineering sites into “data that enables decision-making” by adding feature quantities, and learning how to apply AI in practical work.
We explain a perspective that captures vibration differences that are difficult to see in conventional FFT-based analysis by combining multiple types of information.

Rather than simply reusing data, you will understand how to transform it so that it leads to engineering decisions.
We do not go into complex theory, but specialize in a practical approach that can be reproduced in real work environments.

・ Understand why adding features changes how vibration data is interpreted
・ Learn how to identify vibration differences that FFT alone cannot capture
・ Understand how to restructure Excel data into “decision-making data”
・ Learn data design and application flow for practical AI use
・ Understand the entire process from analysis to decision-making through real examples

• 1. Is your vibration data really “usable data”?
• 　　　– Measurement conditions largely determine the result –
  
  1-1 Effects of measurement conditions, positions, and directions
  1-2 Common unnoticed measurement mistakes in the field
  1-3 Why analysis does not lead to conclusions
  1-4 Fundamental difference between “usable” and “unusable” data
  
  2. How to capture vibrations that cannot be seen with FFT alone
• 　　　– Thinking method to avoid missing subtle “signs” –
  
  2-1 What is and is not visible in conventional vibration analysis
  2-2 How to organize vibration data to reveal differences
  2-3 Why a single indicator is insufficient
  2-4 Perspective of combining multiple pieces of information
• 
  3. Practical techniques for organizing real Excel vibration data
  　　　– Realistic approach without relying on new tools –
  
  3-1 Characteristics and issues of Excel data used in the field
  3-2 Basic concept of AI-ready data preparation
  3-3 Handling variation, missing data, and noise
  3-4 Key points for structuring usable data (practical view)
  
  4. Practical introduction steps for vibration data × AI
  　　　– Thinking to start with minimal effort –
  
  4-1 What is the most important point in AI introduction
  4-2 How to organize and evaluate data
  4-3 Basic flow of AI application
  4-4 How to interpret results for decision-making
  4-5 Thinking method for using AI as “decision support”
• 5. Typical failure patterns in AI introduction in vibration engineering
  　　　– Understanding why it does not work in advance –
  
  5-1 Not a technical problem but a “thinking problem”
  5-2 Typical cases of misuse in real environments
  5-3 Overexpectation from AI
  5-4 Key differences from successful implementations

• 
  8. Q&A