Locomotive Fault Diagnostics
Type: Project
Client: Iran Railways
Year: 2015
Overview
Developed an intelligent monitoring system for DC electric motors in General Motors (GM) locomotives using AI-based fault detection.
Technical Approach
- Vibration analysis for motor condition assessment
- Discrete Wavelet Transform (DWT) for signal processing
- Learning Vector Quantization (LVQ) neural network for fault classification
Results
- Achieved 90% accuracy in early fault detection
- Real-time fault diagnostics capability
- Reduced unplanned downtime
Impact
More efficient and accurate method for monitoring and maintaining locomotive DC traction motors, improving safety and operational efficiency.