Summary
Equipment management in smart manufacturing evolves from reactive “fix when broken” to data-driven full-lifecycle management spanning planning, procurement, installation, operation, maintenance, retrofit, and disposal. The TPM (Total Productive Maintenance) methodology provides the organizational foundation with eight pillars and seven-step autonomous maintenance. OEE (Overall Equipment Effectiveness) serves as the core metric (availability x performance rate x quality rate), with world-class target at 85%. Data acquisition uses PLC/OPC UA, MQTT, and edge gateways to collect status, production, process, alarm, and energy data.
Key Claims
- Maintenance strategies have evolved through four generations: corrective (BM), preventive (PM), condition-based (CBM), and predictive (PdM), with each generation reducing unplanned downtime and maintenance costs.
- Key performance indicators include MTBF, MTTR, equipment availability, maintenance cost ratio, and spare parts turnover rate, all tracked through digital equipment management systems.
- Equipment management must integrate with APS (maintenance schedules as scheduling constraints), MES (real-time status for dispatch), quality control (equipment precision affects SPC), and digital twins (virtual commissioning and maintenance simulation).
Connections
- DataWarehouse — equipment data (status, alarms, maintenance records) flows into the warehouse for cross-system analysis
- DataGovernance — standardized equipment coding, fault classification (ISO 14224), and maintenance procedures are governance essentials
- SparkPerformance — real-time processing of high-frequency sensor data for equipment monitoring parallels streaming performance challenges