One industry that can expect to see unprecedented savings from AI is manufacturing. While most manufacturers are already using some form of preventive or predictive maintenance, AI can usher in a new era of productivity.
You might be wondering: How do data, machine learning and AI fit into the current maintenance system I’m already using? Let’s break down a few common maintenance types and the role AI plays in each.
AI’s Role in Total Productive Maintenance
Total Productive Maintenance (TPM) is a holistic system of maintaining and improving critical assets and operational processes that results in fewer breakdowns, less downtime, increased production and improved safety. Developed in the 1960s, many industrial enterprises use this method today to proactively complete machine maintenance based upon historical data and timetables of when repairs are expected to be needed. Using concepts of planned maintenance, TPM aims to improve Overall Equipment Effectiveness (OEE) and plant productivity. With regular equipment maintenance, you can avoid breakdowns and increase uptime of assets.
AI and Autonomous Maintenance Adoption and Implementation
One of the core features of TPM is Autonomous Maintenance (AM). This type of maintenance makes everyone responsible for machine performance and upkeep. Equipment maintenance is carried out by the machine operators themselves, instead of maintenance technicians being the only ones to repair assets. By having machine operators perform regular maintenance on assets, technicians are freed up to focus on larger adjustments to improve overall machine reliability. AM is often challenging to implement because it takes a great deal of communication and training. Machine operators lack the historical machine knowledge that technicians have, and technicians might not be so quick to give up certain tasks without foresight into new job duties on the horizon.
Now, businesses can take advantage of AI-driven software that makes adoption of AM easier. Operators on the front line can understand their machines even better than before. Having all your historical data in one easy-to-access dashboard keeps everyone at your company on the same page and makes it easier for machines to get serviced, faster. Now, businesses can ensure that each operator has the right tools and the right knowledge at the right time to get the job done.
Key Differences of Planned Preventive Maintenance vs. Predictive Maintenance
Planned Preventive Maintenance (PPM), also known simply as planned maintenance, is maintenance that is driven by time or events that necessitate repair. A main component of TPM, this type of system means maintenance is scheduled while machines are still working in order to prevent unplanned downtime and maximize the lifespan and productivity of equipment. While effective, there are certain drawbacks to this method. It’s not an exact science, you run the risk of over-maintaining or under-maintaining your assets, and it relies on guidelines for routine checkups but doesn’t take into account contextual information.
Predictive Maintenance uses condition-based indicators and alerts to surface maintenance needs only when your trucks are at risk of breaking down —- optimizing your maintenance cadence and maximizing vehicle availability. For example, a car will alert you when the engine is in danger of overheating outside of the planned maintenance schedule. This type of maintenance is proactively performed when your vehicles are still working but at high risk of failure.
Data and AI in Preventive and Predictive Maintenance
As connectivity and data accessibility become cheaper and more widespread in industry, many companies are looking to predictive maintenance, or condition-based, maintenance, powered by machine learning and analytics.
PPM is largely driven by time-based data. For example, on a car, maintenance is determined by the amount of time passed or mileage driven to determine when maintenance needs to be done. This data also compares how a specific asset is performing compared to the rest of your like assets. Data simply tells you what might happen. Most maintenance technologies focus on transporting data, not aggregating it into real-time analytics. But sending the data is just the first step — what you do with that data is what really matters. AI and machine learning can help aggregate and make use of your data, faster.
Predictive maintenance uses data from various sources like historical maintenance records, sensor data from machines, and weather data to determine when a machine will need to be serviced. Leveraging real-time asset data plus historical data, operators can make more informed decisions about when a machine will need a repair. Predictive maintenance takes massive amounts of data and through the use of AI and predictive maintenance software, translates that data into meaningful insights and data points — helping you avoid data overload.
Sensor data and machine learning models are making it possible to quickly extract more value from large volumes of messy data. Predictive maintenance tools upgrade your existing maintenance systems by using AI to ensure that your people have the right knowledge and tools to keep your mission-critical assets running at peak performance.
