원문정보
초록
영어
This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.
목차
1. Introduction
2. Related Research
2.1 Vibration Analysis of Machines
2.2 Domestic and International AI Technology Trends
2.3 Failure Prediction and Anomaly Detection
2.4 AIOps (Artificial Intelligent for IT Operations)
3. Research Methodology
3.1 Research Objectives
3.2 Duty-free Oil Misuse Detection Model Based on Vibration Data Extracted by 3-axis Gyro Sensor
3.3 Anomaly Detection Model for Agricultural Machinery Based on C-LSTM Neural Network
3.4 Auto Encoder Neural Network Based Anomaly Detection Model
3.5 Historical Data Collection Model
4. Experimental Results
4.1 Key Performance Indicators
4.2 Evaluation Method of Quantitative Targets
4.3 Evaluation Environment for Quantitative Target Items
5. Conclusions and Implications
References