Predictive maintenance uses data analysis and advanced technology to anticipate equipment failures and enable proactive maintenance. It aims to prevent unplanned downtime, optimise maintenance schedules, and reduce operational disruptions. When integrated with Total Productive Maintenance (TPM), a holistic maintenance philosophy, it becomes a powerful tool for improving equipment reliability and overall operational efficiency.
Key Components of Predictive Maintenance in TPM:
Data Collection: Predictive maintenance collects data from sensors, equipment readings, and historical performance records. This data is then analysed to identify patterns and anomalies.
Advanced Analytics: Advanced analytics can predict equipment failures using machine learning and AI by analysing patterns in data.
Condition Monitoring: Predictive maintenance involves monitoring the condition of equipment in real time. This can include measuring variables such as temperature, vibration, pressure, and more.
Thresholds and Alarms: Condition monitoring triggers alarms or notifications when measurements exceed preset thresholds, indicating a potential issue.
Data Integration: Predictive maintenance integrates with maintenance management systems to create work orders or schedule maintenance activities when issues are detected.
Integration with Total Productive Maintenance (TPM):
TPM is a comprehensive maintenance strategy that involves all employees in maintaining and improving equipment and processes. It emphasises proactive and preventive maintenance practices. When predictive maintenance is integrated into TPM, several benefits emerge:
Reduced Downtime: Predictive maintenance minimises unexpected breakdowns, reduces downtime, and increases overall equipment availability.
Optimised Maintenance: TPM's focus on eliminating waste and inefficiency aligns well with predictive maintenance's goal of optimising maintenance schedules and resource allocation.