1. 🚨 Topic: From "Wait for Repair" to "Know Before It Breaks": Why PdM is Changing the Manufacturing Game?
Content description:
Compare strategies: Explain the differences between traditional maintenance:
Reactive Maintenance (Repair when broken): High cost, unplanned downtime.
Preventive Maintenance (Repair on time): Wastes resources, may repair too soon even though the machine is still good.
Predictive Maintenance (PdM): Perform maintenance only when necessary , based on the actual condition of the machine, to ensure it can operate to its full life before it breaks down.
Core Value: Minimize Unplanned Downtime and Extend Machine Life
2. 📡 Main components: Data Pipeline and symptom detection sensors
Content Description: Explains how PdM relies on a complete data flow:
Front row: Sensors: Install detection devices on machinery (both old and new) to collect important data in real-time, such as:
Vibration: The first sign of bearing wear or misalignment.
Temperature: Abnormally high heat indicates a mechanical or electrical malfunction.
Current/Power Consumption: Increased power consumption may indicate an unusually heavy load.
Data transmission: Data from sensors is transmitted through the IoT Gateway to a central processing system.
3. 🧠 Key Points: AI and Machine Learning in Data Analysis
Content Description: Explains the role of AI in turning raw data into insights:
Model Training: AI (especially Machine Learning) learns "normal patterns" and "anomalous patterns" from massive amounts of historical data.
Anomaly Detection: AI can detect subtle deviations from normal patterns in real-time data, providing early warning signals that humans might not otherwise detect.
Prediction: AI uses predictive analytics to calculate a part's "Remaining Useful Life" (RUL), enabling maintenance teams to accurately plan repairs in advance.
4. 🛠️ Value Creation: Precise and Cost-Effective Maintenance Planning
Content Description: Focus on results that are directly beneficial to the business:
Optimal Scheduling: Maintenance teams are automatically notified of upcoming issues with RUL information to enable optimal repair scheduling (Just-in-Time Maintenance).
Reduce costs: Reduce emergency repair costs and optimize spare parts inventory.
Improved safety: Preventing major machinery breakdowns reduces safety risks in factories.
| Strategy/Maintenance | PredictiveMaintenance , PdM , Predictive Maintenance , MaintenanceStrategy , ConditionMonitoring |
| Core technology | AI (Artificial Intelligence), Machine Learning , Sensors , IoT , Data Analytics |
| Application | Manufacturing , Industry40 , Smart Factory , Industrial |
| Results/Benefits | Reduce Downtime , Know Before You Lose , RUL (Remaining Useful Life), Reduce Costs , Optimization (Increase Efficiency) |
| Keywords | Machinery , data analysis , forecasting , anomalies |
Illustration 1: "Predictive Maintenance: Know Before It Breaks"
A split image. On one side, a broken machine with tools scattered around, depicting reactive maintenance. On the other side, a healthy, operating machine with data streams and an AI brain icon, representing proactive monitoring. A clear dividing line or arrow shows the shift.
Text on Image: "Predictive Maintenance: From Reactive to Proactive. Know Before It Breaks."
Illustration 2: "Sensors: The Machine's Senses"
A close-up of various types of sensors (vibration, temperature, current clamp) attached to different parts of an industrial machine (motor, bearing, gear). Data streams flow from the sensors towards a central point.
Text on Image: "Sensors: The Machine's Senses. Gathering Real-time Data."
Illustration 3: "AI & Machine Learning: Unlocking Insights"
A complex network of data points and lines converging into an AI brain or a machine learning algorithm visual. On one side, raw sensor data. On the other, clear charts showing anomaly detection and Remaining Useful Life (RUL) predictions.
Text on Image: "AI & Machine Learning: Unlocking Insights from Data."
