물류 회전설비 고장예지 시스템

• 영문명: A Fault Prognostic System for the Logistics Rotational Equipment
• 발행기관: 한국산업경영시스템학회
• 저자명: 김수형 볘르드바에브 예르갈리 조형기 김규익 김진석
• 간행물 정보: 『산업경영시스템학회지』제46권 제2호, 168~175쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

목차

1. Introduction

2. Model Design

3. Data Preparation and Transformation

4. Training Model

5. Model Evaluation

6. Concluding Remarks

Acknowledgement

References

키워드

Machine Learning Anomaly Detection Fault Diagnosis Vibration Signal Frequency Analysis