A Fault Prognostic System for the Logistics Rotational Equipment

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 2호
ㆍ저자명 : Soo Hyung Kim, Berdibayev Yergali, Hyeongki Jo, Kyu Ik Kim, Jin Suk Kim

목차

1. Introduction
2. Model Design
2.1 Design of Fault Monitoring System
2.2 Design of AI Fault Diagnosis Model
3. Data Preparation and Transformation
3.1 Data Acquisition
3.2 Noise Filtering
3.3 Feature Extraction for Anomaly Detection
3.4 Feature Extraction for Fault Diagnosis
4. Training Model
4.1 Training Dataset for Anomaly Detection Model
4.2 Anomaly Detection Model Algorithm
4.3 Training Dataset for Fault Diagnosis Model
4.4 Fault Diagnosis Model Algorithm
5. Model Evaluation
6. Concluding Remarks
Acknowledgement
References

영어 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.

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