Predictive maintenance is a maintenance strategy that uses data and analytics to predict when equipment or machinery is likely to fail, so that maintenance can be performed before the failure occurs. The goal of predictive maintenance is to reduce downtime and improve equipment reliability, while also reducing the cost of maintenance.
There are several key components to a predictive maintenance program, including:
- Data collection: Data is collected from equipment sensors, IoT devices, or other sources to monitor equipment performance, usage, and condition. This data can include information such as temperature, vibration, and oil analysis.
- Data analysis: The collected data is analyzed using advanced analytical techniques such as machine learning and statistical models to identify patterns and trends that indicate when equipment is likely to fail.
- Maintenance scheduling: Based on the analysis of the data, maintenance is scheduled at the most appropriate time, rather than on a fixed schedule. This can help to minimize downtime and prolong the lifespan of equipment.
- Remote monitoring: Predictive maintenance can be used to monitor equipment remotely, allowing maintenance teams to identify potential issues and schedule repairs before they become major problems.
- Root cause analysis: Predictive maintenance can be used to identify the root cause of equipment failure, which can then be used to improve equipment design and prevent future failures.
Overall, predictive maintenance can significantly reduce the costs of maintenance and downtime, improve equipment reliability and performance, and increase the overall efficiency of an organization. It can be applied to various industries including manufacturing, oil and gas, transportation and logistics, and many more.
|Predictive maintenance — recommended articles|
|Quality 4.0 — Ai in manufacturing — Real-time data collection and analysis — Digital twin — Preventive and predictive maintenance — Analytical sheet — Machine Learning — Maintenance in industry — Smart factory|
- Zonta, T., Da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889.