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Predictive Machine Learning

Published research implementation

Predictive Maintenance and Fault Detection

Problem

Industrial equipment failures surface late, when sensor drift has already turned into downtime.

System

A predictive-learning system that validates and preprocesses sensor time series, engineers temporal features, and applies residual and CRNN-style models to produce calibrated early-fault risk scores that feed maintenance alerts. It implements the direction of my published work on early-warning industrial fault detection.

My contribution

Designed the time-series preprocessing and feature pipeline, implemented and calibrated the fault-detection models, and built the evaluation pipeline used to report early-warning performance.

System architecture

  1. Sensor data
  2. Validation and preprocessing
  3. Feature pipeline
  4. Time-series model
  5. Risk score
  6. Alert or maintenance recommendation

Technical focus

  • Predictive maintenance
  • Fault detection
  • Model calibration
  • Imbalanced learning
  • Anomaly detection

Technology stack

  • PyTorch
  • Scikit-learn
  • Pandas
  • NumPy
Publication related to Predictive Maintenance and Fault Detection

Data source

Dataset and data-collection write-up is being prepared.

Data pipeline

Ingestion and preprocessing write-up is being prepared.

Model or LLM workflow

Model and workflow write-up is being prepared.

Evaluation and results

Evaluation setup and results write-up is being prepared.

Challenges and engineering decisions

Key engineering decisions write-up is being prepared.