How AI is Making Predictive Maintenance a Reality for the Industrial IoT

Today’s industrial companies have more data at their disposal than ever before. But when it comes to their most critical assets and product quality, how should businesses be leveraging data to predict and prevent problems before they happen?

With the rise of industrial artificial intelligence (AI) and the Internet of Things (IoT), businesses in all industries are being reimagined with software. Companies are learning how to use their data to not only analyze the past but to predict the future as well. Although IoT has produced a wide range of new revenue opportunities for industrial businesses, they might be overlooking untapped value hiding within their four walls.

Asset maintenance is a key area that can drive major cost savings and production value for industrial businesses around the world. The cost of downtime is high: $647 billion is lost globally each year within individual industries when assets, machines, equipment and systems are unavailable. Over the years, businesses have tried with limited success to overhaul their maintenance processes to decrease unplanned downtime and increase uptime. Today, more industrial data is available than ever before, but there is still a lack of clarity around the best way to use it in the quest to maximize operational efficiency and productivity. As little as 1 percent of industrial data gets used. As more machines come online, not using the other 99 percent costs dollars and missed opportunities.

With industrial AI and machine learning, global industry now has the ability to process massive amounts of sensor data at an unprecedented rate. This opens up new, viable avenues for businesses to improve upon existing maintenance operations and even add something new: predictive 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 Original Equipment Manufacturer (OEM) timetables of when repairs are expected to be needed. Using the concept of Planned Maintenance (PM; further explained below), TPM aims to improve Overall Equipment Effectiveness (OEE) and plant productivity. With regular equipment maintenance, you can avoid breakdowns and increase the 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 having foresight into new job duties on the horizon.

Businesses can now take advantage of industrial AI software that makes it much easier to benefit from AM. Operators on the front line can understand their machines even better than before. Having all historical asset data, which typically exists in multiple systems, in one easy-to-access dashboard keeps everyone on the same page and makes it easier and faster for machines to get serviced. Forward-thinking companies can ensure each operator is equipped with the right knowledge and tools at the right time to get the job done.


Planned Preventive Maintenance (PPM), also known simply as Planned Maintenance (PM), is maintenance that’s driven by time or events that necessitate repairs. 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 to maximize the lifespan and productivity of equipment. While effective, there are certain drawbacks to this method. While two of the same type of assets might be used for the same job, those two assets don’t break the same and ultimately don’t need the same maintenance schedule. It’s not an exact science — you run the risk of over-maintaining or under-maintaining your assets, and it relies on OEM guidelines for routine checkups but doesn’t take into account unique signals from individual assets or contextual information about the environment, weather or operating conditions.

Predictive Maintenance uses condition-based indicators and alerts to surface maintenance needs only when assets are at risk of breaking down — optimizing maintenance cadences and maximizing asset availability. By analyzing data generated by machines and sensors, anomalies and issues can be detected early on before they turn into bigger problems. Potential failures can be predicted and prevented by interpreting signals that are unique to individual assets — instead of using uniform maintenance approaches by asset type — to inform precise, proactive repairs that save time and money. To be clear: These are not a bunch of warnings that are mainly ignored by operators, but rather actionable insight based on the individual asset’s unique data.

What does predictive maintenance look like in the manufacturing industry? Let’s consider a potential use case in the automotive sector, where unplanned downtime can cost automakers a staggering $22,000 per minute. Automakers can unlock the predictive potential of their data to prioritize their planned maintenance schedules based on asset failure modes. Using the predictive, actionable insights generated by industrial AI and machine learning engines — which, for example, can analyze real-time and historical data at scale from a critical component type across assets to identify unique warning signals and prioritize the risk of failure by machine — automakers can effectively reduce their unplanned downtime.

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 its like types. 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, fault and sensor data from machines, and contextual data such as temperature or humidity to determine when a machine will need to be serviced. Leveraging real-time asset data plus historical data, operators can make better-informed decisions about when a machine will need repair. Predictive maintenance takes massive amounts of data. Industrial AI and machine learning translate that data into meaningful intelligence and actionable insights.

With the increasing availability and affordability of connectivity, sensors, computing power, and industrial AI and machine learning, past barriers to entry have been eliminated. It’s never been more viable to start capitalizing by extracting value from large volumes of messy data. Predictive Maintenance tools upgrade your existing maintenance program by ensuring your workforce has the right knowledge and tools to keep mission-critical assets up, running and producing at peak performance.

Summary: AI’s Role in Optimizing Maintenance Systems and Processes
Summary: AI’s Role in Optimizing Maintenance Systems and Processes

Here’s the key to implementing successful maintenance operations that support your business needs: Understanding what questions you need answered and how data can help you get those answers. Do you need to know simply what’s already occured so you can plan and budget for next year? Or do you need to know how to prevent unplanned downtime, lower your costs and expedite repairs?

As digitization continues to transform businesses end to end, optimizing operations by leveraging not only Preventive Maintenance tools, but Predictive Maintenance tools as well, is becoming table-stakes to survive. Technology is not a luxury anymore, it is needed to remain competitive, reduce downtime, improve safety and increase profits.