1. Internet of Things (IoT) and Sensor Technology: Predictive maintenance uses IoT-enabled sensors to collect vast amounts of data about vehicle performance, allowing for early detection and diagnosis of potential problems.

2. Data Analytics: Through advanced data analysis techniques, vehicle performance data can be used to create predictive models that identify factors affecting vehicle health, detecting and addressing health issues before they cause more significant problems.

3. Machine Learning: Predictive maintenance uses machine learning algorithms that analyze historical data on vehicle performance to identify underlying patterns and predict future failures.

4. Artificial Intelligence (AI): AI-powered predictive maintenance systems can analyze real-time data from multiple sources to develop highly accurate models that predict vehicle performance issues.

5. Predictive Maintenance Software: Predictive maintenance software is a toolset that includes machine learning, AI, and data analytics that allows organizations to optimize vehicle performance and reduce unplanned downtime.

6. Predictive Analytics Platform: Predictive maintenance platforms help businesses achieve maximum asset utilization by monitoring vehicle health and maintenance schedules in real-time.

7. Cloud Computing: Cloud-based predictive maintenance solutions allow systems to process data in real-time and support predictive maintenance analytics.

8. Equipment Health Monitoring: Predictive maintenance systems monitor vehicle components, such as engines, transmissions, and brakes, to identify potential problems and maintenance needs.

9. Computerized Maintenance Management System (CMMS): A CMMS system is a software package that helps companies manage their maintenance and repair operations. The data generated by a CMMS system can be used for predictive maintenance analytics.

10. Fault Detection: Predictive maintenance systems automatically detect faults in vehicle components, allowing maintenance personnel to address issues early before they cause failure or require more significant repairs.