Project Insights
Intelligent Motor Health Monitoring System
Key engineering insights from our work—what we learned, why it mattered, and how it guides our decisions.
Key Engineering Insights
The most important lessons we're learning and applying as we design, build, and test our system.
1. Multi-Sensor Fusion Improves Early Anomaly Detection
Combining current and acoustic signals enhances our ability to detect deviations from normal operation.
2. Reliable Data Comes Before Machine Learning
Before developing anomaly detection algorithms, we prioritized collecting clean, repeatable current and acoustic data. Quality data is the foundation for meaningful insights.
3. Cross-Disciplinary Collaboration Strengthens Engineering
Combining expertise in electrical, software, mechanical, and hardware engineering brought different perspectives to the table and helped us make better design decisions.
4. Engineering Is an Iterative Process
Our design evolved through discussion, experimentation, and testing. Each iteration clarified assumptions, revealed new challenges, and led to practical improvements.
5. Start Simple, Then Make It Robust
We began with a minimal viable system to validate our approach. As we gained confidence, we improved robustness, expanded capabilities, and refined performance.
