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Research on state perception of scraper conveyor based on one-dimensional convolutional neural network
Jie Lu1,2, Zhenlin Liu1, Chenhui Han1
1School of Coal Engineering, Datong University Shanxi Province, Datong, China.
Plos One
|October 18, 2024
Summary
This study introduces a new method using one-dimensional convolutional neural networks (1DCNN) to assess scraper conveyor health. The 1DCNN model accurately identifies equipment health status, improving mining operations.
Area of Science:
- Mining Engineering
- Artificial Intelligence
- Machine Learning
Background:
- Current scraper conveyor health assessments rely heavily on expert knowledge.
- Establishing accurate degradation models for mining equipment is challenging.
- Objective health status monitoring is crucial for operational efficiency and safety.
Purpose of the Study:
- To develop an automated method for assessing scraper conveyor health status.
- To overcome limitations of subjective expert-based assessments.
- To provide a data-driven approach for real-time equipment health monitoring.
Main Methods:
- Utilized four preprocessed monitoring signals from scraper conveyors.
- Developed and applied a one-dimensional convolutional neural network (1DCNN) model.
- Extracted effective features and mapped them to equipment health states.
Main Results:
- The proposed 1DCNN method achieved a high accuracy rate of 98.9% in identifying health status.
- Demonstrated effective feature extraction and health status recognition capabilities.
- Comparative analysis confirmed the method's superior performance.
Conclusions:
- The 1DCNN-based method offers an effective solution for scraper conveyor health assessment.
- Provides valuable technical support for health management in coal mining.
- Enables more reliable and objective monitoring of mining equipment.
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