What Is Predictive Maintenance?
In AI and machine learning, predictive maintenance refers to the ability to use volumes of data to anticipate and address potential issues before they lead to breakdowns in operations, processes, services, or systems. Having strong predictive maintenance tools in place enables businesses to anticipate when and where potential breakdowns in service can occur and move to respond to them in order to prevent potential interruptions in services.
Predictive Maintenance vs. Preventive Maintenance
Predictive maintenance is similar to preventative maintenance. Both are types of scheduled maintenance. However, preventative maintenance involves general best practices for care of equipment, without knowing any specifics of how the item was used. Predictive maintenance utilizes actual measured usage, operating conditions, and equipment feedback to generate individualized predictions of impending issues.
Why Is Predictive Maintenance Important?
Implementing predictive maintenance services enables organizations to maintain critical assets for as long as possible in order to ensure that systems remain operational. This allows organizations to use their existing data to stay a step ahead of potential breakdowns or disruptions and address them proactively, instead of reacting to issues as they arise. This includes:
- Lowering costs by reducing unplanned downtime, fewer redundant inspections and ineffective preventative maintenance measures. Savings are incurred from increased productivity and decreased labor and materials costs.
- Reduced equipment lifecycle costs through improved performance and extended equipment life.
- Indirect benefits, including improved quality, reduced rework, reduced defects, improved safety and increased energy efficiency.
According to data from McKinsey, predictive maintenance tools can reduce manufacturing machine downtime by 30 to 50 percent and increase machine life by 20 to 40 percent. Manufacturers can also improve their operations and keep their supply chains intact.
Predictive Maintenance Use Cases
Predictive maintenance holds significant potential to enhance the efficiency and productivity of several verticals that rely on assets requiring frequent repair.
Manufacturers can use predictive maintenance techniques to implement safeguards that notify the right people when a piece of equipment needs to be inspected. Using their existing historical data, such as electrical current, vibration, and sound generated by equipment, manufacturers can build models to anticipate the likelihood of a potential breakdown before it occurs. These models can identify which equipment is at greatest risk of failing, allowing maintenance teams to respond accordingly. The insights from the models fit to historic data can also help point to the root cause of the problem and inform operators of underlying issues.
Supply chain operators can also use predictive maintenance analytics to plan around equipment downtime and potential disruptions. Model insights can inform the supply chain team how long an asset, system, or component could be offline, allowing them to plan accordingly.
Original equipment manufacturers (OEMs) can provide predictive maintenance as a service. By collecting data from multiple customers’ equipment, OEMs can build models with data collected from the wider customer base to provide individual customers with insights and equipment-specific maintenance schedules.
Government agencies can also benefit from implementing proper predictive maintenance techniques. Automated machine learning for predictive maintenance can help officials understand when new parts, components, and overhauls will be required for military equipment like helicopters, aircraft, and weapons systems. Using predictive maintenance models that rely on AI and machine learning can help public sector agencies operate more efficiently, keep expensive assets in usage longer, and enhance supply chain operations.
DataRobot can help government and other public sector officials address time-consuming Failure Mode, Effects, and Criticality Analysis (FMECAs) by running models that can predict patterns based on different assets’ environments. These predictive maintenance models can lead to more accurate asset and component lifespans and can be deployed for other use cases, including accident analysis and labor optimization.