Condition-based preventive maintenance is a type of maintenance strategy that aims to prevent equipment failure by monitoring equipment condition and performing maintenance only when necessary.
This approach is based on the idea that equipment failures are often preceded by specific signs or symptoms, which can be detected by monitoring and analyzing various data points.
The process of condition-based preventive maintenance typically consists of four main steps:
- Data collection: data is collected from a variety of sources, such as sensors, equipment monitoring systems and maintenance records. This data is used to establish a baseline of normal equipment operating conditions.
- Data analysis: The data collected is analyzed to identify any deviations from normal operating conditions. This analysis can be performed using a variety of tools and techniques.
- Condition monitoring: once deviations are identified, the equipment is continuously monitored to track the progression of the deviation and determine when maintenance is required.
- Maintenance action: when maintenance is required, it is performed based on the condition of the equipment rather than on a predetermined schedule. It may consist of replacing worn parts, adjusting settings or cleaning the equipment.
Advantages of condition-based preventive maintenance
The advantages of condition-based preventive maintenance are increased equipment reliability and availability, reduced maintenance costs and improved safety. By performing maintenance only when necessary, resources can be allocated more efficiently, reducing downtime and extending equipment life.
Overall, condition-based preventive maintenance is a proactive approach to maintenance that can help companies optimize their maintenance activities and improve the overall performance of their equipment.
4Action, the Zerintia Technologies solution for condition-based preventive maintenance
is Zerintia Technologies
platform that enables condition-based preventive maintenance by orchestrating data generated in the industrial plant from different sources, such as Operational Technologies (OT), Information Technologies (IT) and employee-related information, for real-time event detection.
Based on this data and the application of these conditions through a rules engine, actions are triggered in response to these events, involving employees, machinery and information systems.