Predictive Maintenance
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Predictive maintenance
Predictive maintenance leverages real-time data from sensors to track asset performance and historical data stored in, for instance, a Computerized Maintenance Management System (CMMS) to determine machine health. It applies algorithms to data to find trends using leading indicators, such as temperature, vibration, electrical, pressure, and other measurements that indicate when a failure is expected to happen.
Preventive maintenance (PM) establishes a fixed maintenance schedule for each machine, regardless of its present condition. Maintenance intervals are either calendar-based or usage-based and scheduled according to manufacturer recommendations.
Preventive maintenance reduces the incidence of reactive maintenance and unplanned downtime and increases safety by ensuring equipment is serviced regularly. There are two types of PM actions: scheduled restoration and scheduled discard. Both measures are carried out by the PM task(s) prescribed to address the precise failure mode.
In the long term, emerging technologies will enable prescriptive analytics. For example, machine learning (ML) and artificial intelligence (AI) will be able to diagnose themselves and communicate when to perform certain “restorative” or “discard” preventive maintenance tasks.