An enterprise-grade AI predictive maintenance system that forecasts machine failure risk, estimates Remaining Useful Life (RUL), and provides explainable AI-driven operational insights using sensor telemetry data.

This platform combines machine learning, fleet intelligence monitoring, SHAP explainability, and interactive Streamlit dashboards to simulate Industry 4.0 predictive maintenance workflows.

Modern industrial systems generate massive volumes of sensor telemetry data from engines, turbines, and manufacturing equipment. Unexpected failures can lead to:

• production downtime
• maintenance cost escalation
• operational disruption
• supply-chain delays
• equipment safety risks

This project builds an AI-powered predictive maintenance platform capable of:

✅ Predicting machine failure risk
✅ Estimating Remaining Useful Life (RUL)
✅ Monitoring fleet-wide operational health
✅ Explaining AI predictions using SHAP
✅ Providing maintenance recommendations
✅ Supporting Industry 4.0 operational intelligence

This project simulates Industry 4.0 predictive maintenance workflows using explainable AI, fleet intelligence dashboards, and machine-level operational risk monitoring.

The platform is designed to resemble real-world industrial AI systems used in:

• manufacturing analytics
• aerospace engine monitoring
• industrial IoT platforms
• operational intelligence systems
• predictive asset maintenance

• Predict machine failure probability
• Estimate Remaining Useful Life (RUL)
• Identify high-risk engines
• Detect operational degradation patterns

Interactive Streamlit dashboard with:

• fleet-wide KPIs
• risk segmentation
• machine-level drill-down
• operational health monitoring
• AI-generated maintenance recommendations

Integrated SHAP explainability for transparent AI decision-making.

Includes:

• SHAP summary plots
• SHAP feature importance
• SHAP waterfall explanations
• per-engine explainability

The platform generates AI-driven maintenance recommendations based on predicted operational risk.

Examples:

• Continue routine monitoring
• Schedule preventive inspection
• Prioritize maintenance intervention
• Escalate critical-risk machinery

This project uses industrial sensor telemetry data commonly used in predictive maintenance and Remaining Useful Life (RUL) estimation research.

• engine sensor telemetry
• operational cycle data
• degradation behavior patterns
• machine health indicators
• failure progression signals

• machine failure prediction
• Remaining Useful Life estimation
• operational risk segmentation
• predictive maintenance intelligence

AI-Predictive-Maintenance/
│
├── dashboard/
│ └── app.py
│
├── data/
│ ├── raw/
│ └── processed/
│
├── models/
│ ├── RF_Regressor_rul_regressor.pkl
│ └── RF_Classifier_failure_classifier.pkl
│
├── outputs/
│ ├── shap_waterfall.png
│ ├── machine_drilldown.png
│ ├── risk_distribution.png
│ ├── dashboard_overview.png
│ ├── dashboard_engine_1_shap_waterfall.png
│ ├── dashboard_shap_summary_plot.png
│ ├── shap_bar_plot.png
│ └── shap_summary_plot.png
│
├── reports/
│ └── executive_summary.md
│
├── src/
│ ├── preprocessing.py
│ ├── feature_engineering.py
│ ├── train_model.py
│ ├── evaluate_model.py
│ ├── explain_model.py
│ └── generate_predictions.py
│
├── requirements.txt
├── README.md
└── .gitignore

• sensor telemetry cleaning
• missing value handling
• scaling & normalization
• cycle normalization
• rolling statistical features

Engineered features include:

• rolling sensor means
• cycle-based degradation indicators
• operational trend signals
• normalized lifecycle metrics

The platform trains machine learning models for:

• machine failure prediction
• Remaining Useful Life estimation
• operational risk scoring

SHAP explainability provides:

• feature impact analysis
• model transparency
• operational interpretability
• engineering trust

The platform integrates SHAP-based explainability to improve operational transparency, engineering trust, and responsible deployment of predictive maintenance systems.

Explainability is critical for:

• industrial AI governance
• maintenance prioritization
• engineering validation
• operational confidence
• AI transparency

Dashboard Overview

Risk Distribution

Machine Drilldown

SHAP Summary

SHAP Waterfall

• Predictive maintenance monitoring
• Fleet reliability intelligence
• Industrial asset monitoring
• Remaining Useful Life estimation
• Maintenance prioritization
• Failure risk forecasting
• Industry 4.0 transformation workflows
• Smart manufacturing analytics
• Industrial IoT monitoring

The AI system can identify:

• engines approaching operational failure
• degradation acceleration patterns
• high-risk sensor signatures
• maintenance prioritization opportunities
• fleet-wide operational trends

This workflow generates:

• trained predictive maintenance models
• Remaining Useful Life predictions
• fleet-level risk segmentation
• SHAP explainability reports
• operational maintenance intelligence dashboards

git clone https://github.com/your-username/AI-Predictive-Maintenance.git
cd AI-Predictive-Maintenance

python -m venv venv
venv\Scripts\activate

python3 -m venv venv
source venv/bin/activate

pip install --upgrade pip
pip install -r requirements.txt

python src/preprocessing.py

python src/feature_engineering.py

python src/train_model.py

python src/evaluate_model.py

python src/predict.py

python src/explain_model.py

streamlit run dashboard/app.py

• Real-time streaming telemetry
• IoT integration
• MLOps pipeline automation
• cloud deployment
• anomaly detection
• edge AI deployment
• maintenance scheduling optimization
• digital twin simulation

This system helps industrial organizations:

• reduce unplanned downtime
• improve asset reliability
• optimize maintenance scheduling
• lower operational costs
• improve equipment lifespan
• increase operational efficiency

This project demonstrates practical experience in:

• machine learning engineering
• predictive maintenance systems
• explainable AI
• industrial analytics
• operational intelligence
• dashboard engineering
• fleet monitoring systems
• Industry 4.0 workflows

Girish Shenoy

AI • Machine Learning • Predictive Analytics • Explainable AI • Industrial Intelligence

Please consider giving this repository a ⭐ on GitHub.