Device Maintenance Prediction
Chunxiao Technology · 2023
Role: AI Algorithm Developer
Experimental predictive maintenance exploration using factory-provided textile equipment records. Applied Spark MLlib Random Forest on historical maintenance logs — limited data constrained accuracy, but gained hands-on experience with Spark MLlib ecosystem, feature engineering pipelines, and industrial data modeling. Did not progress to production deployment.
Random Forest
Approach
3 categories
Features
Experimental
Status
Learning
Value
Problem
Explore whether factory maintenance records can support data-driven predictive maintenance scheduling for textile equipment.
Solution
Spark MLlib Random Forest pipeline: factory maintenance logs → feature engineering (intervals, fault frequency, usage intensity) → aging trend modeling → maintenance window prediction.
Key Highlights
- ▸Designed predictive maintenance workflow using factory-provided textile equipment maintenance records
- ▸Implemented Spark MLlib Random Forest on limited real-world industrial dataset
- ▸Built feature engineering pipeline: maintenance intervals, fault frequency, usage intensity
- ▸Gained hands-on experience with Spark MLlib ecosystem and industrial data challenges
Tech Stack
What I Learned
Industrial data is messier and sparser than research datasets; predictive accuracy is heavily constrained by data volume and quality; Spark MLlib pipelines provide solid foundation for scaling when more data becomes available.