Predictive_Maintenance_Data_Science.pptx

predictive maintenance
data science

1. Understanding Industrial Equipment & Failures A Guide for Data Scientists in Predictive Maintenance Presented by: Ahmad Hodeb

2. Why Predictive Maintenance? 🔥 Reactive Maintenance – Fix it when it breaks ⚠️ (Expensive & Unplanned) 🛠️ Preventive Maintenance – Scheduled maintenance ⏳ (Can be unnecessary) 🤖 Predictive Maintenance (PdM) – Data-driven maintenance 📊 (Optimized & Smart)

3. Common Industrial Equipment 🔹 Rotating Equipment – Motors, Pumps, Turbines, Compressors 🔹 Static Equipment – Boilers, Heat Exchangers, Pressure Vessels 🔹 Electrical Systems – Transformers, Switchgear, Circuit Breakers 🔹 Hydraulic & Pneumatic Systems – Actuators, Valves, Pipelines

4. Types of Failures in Industrial Equipment 🔴 Mechanical Failures – Wear, Misalignment, Fatigue ⚡ Electrical Failures – Insulation Breakdown, Power Surges 💨 Fluid Failures – Leaks, Contamination, Cavitation 🔥 Thermal Failures – Overheating, Creep, Thermal Shock

5. How Sensors Help in Predictive Maintenance 🔹 Vibration Sensors – Detect misalignment, imbalance 🌡️ Temperature Sensors – Identify overheating risks 🔊 Acoustic Sensors – Listen for abnormal sounds 💧 Flow & Pressure Sensors – Monitor leaks and blockages ⚡ Electrical Sensors – Measure current, voltage fluctuations

6. Data Science Meets Predictive Maintenance 🧠 Supervised Learning – Historical failures → Predict future breakdowns 📈 Anomaly Detection – Identify deviations from normal patterns 📊 Time-Series Analysis – Forecast equipment behavior over time

7. Challenges in Predictive Maintenance ❌ Data Availability – Not enough failure data for training 📉 False Alarms – Incorrect predictions lead to wasted resources 🔄 Dynamic Operating Conditions – Machines operate under varying loads 🛠️ Interpretability – Engineers need to trust AI decisions

8. Case Study – Predicting Aircraft Turbofan Engine Failures ✈️ Problem: Unexpected turbofan engine failures lead to costly delays & safety risks 📊 Solution: Monitor vibration, temperature, and pressure sensors for real-time monitoring 🤖 AI Model: Trained on engine sensor data to detect early failure patterns 🚀 Outcome: 40% reduction in unplanned maintenance & improved engine reliability