Predictive Maintenance (PdM) – AI-Driven Strategy for Smarter Asset Care

Predictive Maintenance (PdM) is a data-driven maintenance strategy that continuously monitors the health and performance of critical systems such as EV charging equipment, power converters, and energy storage units. It relies on advanced sensors and real-time data analytics to detect early signs of wear, performance deviations, or potential failures.
Unlike reactive or time-based maintenance, PdM ensures that service actions are performed exactly when needed, not too early and never too late. This proactive approach minimizes unplanned downtime, reduces maintenance costs, enhances operational reliability, and extends the lifespan of EV charging infrastructure. By preventing breakdowns before they occur, Predictive Maintenance plays a vital role in ensuring consistent uptime and user trust across modern EV charging networks.
How It Works
- Data Collection
Sensors and IoT devices collect real-time data on temperature, vibration, pressure, sound, and other performance indicators.
- Data Analysis
AI and machine learning models analyze this data to identify patterns, anomalies, or signs of wear and tear.
- Prediction & Alerts
The system forecasts potential failures or performance dips and triggers alerts or recommendations for proactive maintenance.
- Maintenance Action
Teams intervene only when necessary—just in time to prevent breakdowns or optimize performance.
Applications Across Industries
Future Outlook
With AI and edge computing advancing, Predictive Maintenance is evolving into Prescriptive Maintenance, where systems not only predict failures but also recommend and automate actions. As industries digitize, PdM is becoming essential to Industry 4.0 and smart factory ecosystems.


