Wheel Defect Detection Using a Hybrid Deep Learning Approach
importance 5/10
ai assisted
Machine Fault Diagnosis Techniques rail transport stage: research
Problem
Detecting railway wheel defects early despite interfering vibrations that cause false alarms during operation.
Why it matters
Undetected wheel defects compromise passenger safety and accelerate track damage and maintenance costs.
What the AI did
A hybrid deep learning approach was proposed to accurately detect various wheel defect types in real time despite vibration noise.
Results
not stated in source
Impact
Approach proposed to reduce false alarms; field adoption not stated.
Record
| Venue | Sensors |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Khurram Shaikh, Imtiaz Hussain Kalwar, Bhawani Shankar Chowdhry |
| Citations | 26 (OpenAlex, at import time) |
| References | 29 |
| Model or method | hybrid deep learning (architecture not specified in abstract) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s23146248 |
| Open access | gold |
| Keywords | wheel defects, railway safety, deep learning, vibration, real-time detection |
Importance rating
5
real but narrower advance
Abstract states defective wheels significantly impact railway operational performance and safety, accelerate track damage, and raise maintenance costs; early detection is crucial. A real safety/cost problem but no scale quantified.
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