Predictive maintenance is a strategy that involves real-time data to foresee potential machine failures and enables timely preventative measures. The strategy enables businesses to proactively maintain machines and systems to minimize technology downtime and optimize operational efficiency. It involves predicting potential failures via real-time and historical machine performance data analysis.
Preventive maintenance implies performing regular maintenance tasks on a predetermined schedule. The approach relies on the integration of sensors, data transport mechanisms, and advanced analytics:
The data gathered by the sensors is then transported to a central system, such as a manufacturing execution system (MES), SCADA, facility management software, fleet management software, or Computerized Maintenance Management Software (CMMS). This data transport is typically facilitated through connectivity solutions like cellular IoT.
Once the data is centralized, machine learning algorithms analyze it, recognizing patterns and identifying potential issues or deviations from normal operating conditions. By comparing current data with historical performance data, predictive maintenance can predict when maintenance or inspection is necessary.
Predictive maintenance is employed across various industries and verticals including:
Manufacturing (manufacturing plants’ machinery)
Aerospace (aircraft safety)
Transportation (trains, buses, and other vehicles to avoid breakdowns)
Energy (power plants and renewable energy facilities for uninterrupted power generation)
Oil and Gas (monitoring equipment health) Healthcare (monitoring medical equipment in hospitals and medical facilities)
Mining (prevention of equipment failures in mining)
Smart Building (optimization of HVAC systems, lighting and other components In commercial and residential buildings)
Fleet Management (performance of vehicles in logistics and transportation)
Telecommunications (smooth network operations and prevented service disruptions)
Agriculture (is applied in farming to track the performance of agricultural machinery and equipment)
Reactive maintenance, preventive maintenance, and predictive maintenance are three different approaches that are all utilized in equipment maintenance, but each with different characteristics: Reactive Maintenance has a fix-it-when-it-breaks approach, which means equipment is repaired or replaced only after it fails or breaks down. Preventive Maintenance on the other hand is a planned and proactive approach that involves performing maintenance tasks on a fixed schedule or time-based intervals. Preventive differs from the previous one by being based on historical data, manufacturer recommendations, industry standards, or government regulations. Below you may see a more detailed comparison.
Aspect | Reactive Maintenance | Preventive Maintenance | Predictive Maintenance |
|---|---|---|---|
Approach | Reactive and unplanned | Planned and proactive | Proactive and data-driven |
Maintenance Occurrence | After equipment failure | Time-based intervals | Data-driven predictions |
Goal | Fixing broken equipment | Preventing failures | Preventing failures proactively |
Downtime Impact | High downtime | Reduced downtime | Minimal downtime |
Equipment Lifespan | Shortened | Extended | Extended |
Cost | High costs (emergency repairs) | Predictable costs | Optimal cost management |
Planning | No planning required | Planned schedules | Real-time monitoring and analysis |
Data Usage | N/A | Historical data | Real-time data analysis |
Implementation Complexity | Low | Low | Moderate to High |
Suitable for Critical Equipment | Low criticality | Medium criticality | High criticality |
