Predicting equipment failures is highly beneficial, but with prescriptive maintenance, industry maintenance teams can utilize real-time data, analytics, and machine learning to recommend the best actions to prevent issues, optimize performance, and extend asset life. 

Unlike predictive maintenance, which tells you when something might fail, prescriptive maintenance tells you what to do about it — adjust operating conditions, schedule specific repairs, or take other steps — to reduce downtime, cut costs, and improve reliability. 

With technology from UpTime Solutions, industries can prevent downtime and optimize maintenance strategies.

Prescriptive Maintenance vs. Predictive Maintenance

Maintenance strategies have evolved from a reactive approach — fixing things when they are broken — to proactive, predictive methods that leverage technology to anticipate and prevent failures.

Preventive maintenance schedules maintenance tasks on a fixed schedule, dependent on time or usage, and condition-based maintenance uses sensors to track actual conditions of assets and perform maintenance.

Predictive and prescriptive maintenance are both done before an asset fails based on data that forecasts equipment failures and offers recommendations for actions to prevent failures. 

Although very similar, prescriptive and predictive maintenance differ. Predictive maintenance focuses on when a failure might occur, and prescriptive maintenance offers recommendations on what to do to prevent a failure from occurring. 

The Core Technologies Behind Prescriptive Maintenance

Prescriptive maintenance relies on a variety of advanced technologies and analytical tools that turn raw data into actionable maintenance recommendations.

• IoT Sensors & Connected Devices: These devices are attached to assets and collect real-time data through vibrations, temperature, pressure, sound, energy use, and other machine conditions. 

• Machine Learning (ML) & AI Models: Through IoT sensors, equipment logs, maintenance history, environmental conditions, and more, ML models can create a complete picture of how equipment behaves over time. 

Digital Twins: These virtual replicas of assets simulate how assets behave in real-time. They combine sensor data, machine learning models, and physics-based simulations to mirror actual conditions and performance of equipment to not only predict failures, but test and evaluate best maintenance methods before applying them to the actual asset.