Advancing Agricultural Machinery Maintenance: Deep Learning-Enabled Motor Fault Diagnosis
Condition monitoring and fault diagnosis of the agricultural machinery are critical for ensuring the safety and stability of agricultural production processes. Timely detection of machinery failures, particularly in motor-driven systems, is essential to prevent unexpected shutdowns, maintain operati...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11087541/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Condition monitoring and fault diagnosis of the agricultural machinery are critical for ensuring the safety and stability of agricultural production processes. Timely detection of machinery failures, particularly in motor-driven systems, is essential to prevent unexpected shutdowns, maintain operational continuity, and avoid economic losses. Recent advancements in artificial intelligence (AI), particularly deep learning (DL)-based techniques, have demonstrated significant potential in machinery condition monitoring and fault diagnosis. These technologies offer reliable and efficient solutions for predictive maintenance in agricultural machinery, enabling proactive intervention in the early stages of catastrophic failures. This article reviews the application of DL-based technologies in the motor fault detection of agricultural machinery, highlighting their effectiveness in identifying early-stage anomalies and classifying fault types. The integration of DL-based fault detection systems into agricultural machinery not only enhances diagnostic accuracy but also reduces maintenance costs and downtime. However, challenges remain in implementing these systems, including data acquisition limitations, computational resource requirements, and algorithm adaptability to diverse operational conditions. This article further discusses future research directions, such as optimizing DL models for real-time processing, improving robustness under varying agricultural conditions, and developing user-friendly interfaces for farmers and technicians. By addressing these challenges, AI-driven fault detection systems can significantly contribute to the reliability and sustainability of agricultural production. |
|---|---|
| ISSN: | 2169-3536 |