What Is AI in Predictive Maintenance?

Artificial Intelligence (AI) applies machine learning principles to solve a multitude of service-related problems. Machine learning automates and builds analytics models that can empower your service technicians with predictive actions to prevent potential downtime before it can even occur. AI and machine learning are dynamic systems that produce stronger results with increased exposure to data.

Machine learning is a subset of AI that uses algorithms to understand datasets, effectively “learning,” or building insights. By feeding sample data (also known as training data) to a machine learning system, these algorithms build models that become familiar enough to identify inefficiencies, suggest accuracy improvements, and even make predictions about future outcomes. As the datasets grow, the predictions improve. The predictive applications of machine learning are particularly relevant to maintenance and service use cases. Machine learning can assess and historical and current equipment usage data to predict likely future events—such as the need for a service visit, or the breakdown of equipment if the current situation if left unaddressed. Over time, as the volume of product and usage data increases, these predictions become increasingly accurate and granular.

Unplanned downtime reduces productivity and requires costly truck rolls to resolve. AI can predict equipment problems and make your products more reliable in the field by helping you:

• Predict with what you know: Merge your historical performance data, engineering specs, and real-time analytics to create user-specific, condition-based alarms and alerts so you can fix an issue before it occurs. This is usually the first stage of AI-enabled predictions.

• Predict with what you learn: Whether you have an established IIoT program or are newly connected, AI can help focus and refine your data to create more accurate and effective models over time. Create a predictive maintenance strategy that continuously builds knowledge and identifies triggers that predict downtime and can be proactively resolved.

• Predict with simulation: Simulate the same stress that causes performance problems during the design process to ensure machines stand up to real-world conditions and determine predictive alarm and alert points. By using data collected using AI-enabled processes, you can even improve the simulations you run over time.

People often confuse predictive and preventative maintenance, however, there are significant differences between the two.

Preventive maintenance occurs at regular intervals based on the machine’s lifecycle, regardless of usage, to ensure that no issues emerge. For preventative maintenance, the only variable used to predict failure is the span of time since the previous maintenance was conducted. Regularly scheduled oil changes for your car are a familiar example of preventative maintenance. While preventative maintenance is a huge improvement over reactive service, it has its limitations. Essentially, it is using a very simple data model (e.g., car engines experience long-term problems if oil isn’t changed every 5,000 miles of travel), to make a very broad recommendation (e.g., you should bring your vehicle in ASAP for an oil change). A preventative maintenance model doesn’t factor in any conditions that are unique to your car’s engine, or how the vehicle has been driven.

Predictive maintenance continuously analyzes the condition of connected assets and equipment. It can collect multiple types of data to build detailed models that reflect equipment status, and how that equipment is being used. This way, data produced during the machine’s normal operations is analyzed and to propose much more accurate maintenance recommendations. This data is analyzed to reduce the probability of failures, and provides a richer understanding of the causes, likelihood, and time-to-failure if an asset remains without service. Unlike preventative maintenance, which consists of blanket rules, predictive maintenance delivers accurate and specific recommendations that reflect your equipment and how you’re using it. This accuracy also prevents unnecessary maintenance activities that can incur costs and downtime.

By applying the transformative combination of IIoT and AI to build very robust predictive maintenance data models, organizations are experiencing decreased downtime, greater work productivity, reduced field service costs, improved product design, and heightened worker safety.

Using AI, predictive maintenance models evaluate many variables that reflect an asset’s current status, make predictions based on usage trends, and inform maintenance teams of potential equipment failures in advance. User-specific alarms and alerts can help you prevent problems before they occur, meaning customers can reduce the need for truck rolls and respond to issues with agility. AI-fueled predictive maintenance can yield:

• A 30% drop in unplanned downtime

• 83% faster service resolutions

• 75% less time on site

When AI is used to predict when equipment problems will occur, predictive maintenance can be planned around workers’ schedules. When workers are not disrupted due to an unexpected malfunction or regularly scheduled service visit, customers experience:

• Maximized uptime and fewer productivity lags

• Increased asset utilization

By accurately predicting when a piece of equipment might experience a malfunction or breakdown, you can avoid placing service technicians in hazardous situations. These integral predictions can ensure:

• Workers are a safe distance from machines that are likely to experience malfunctions

• Service technicians can resolve issues before they result in machines becoming dangerous

Additionally, because predictive maintenance can give you the data to save customers up to millions of dollars in reduced downtime, customer satisfaction will increase, leading to higher renewal rates, lower churn, and better net promoter scores. Simply put, the shift to predictive maintenance results in reduced service needs, faster and less disruptive service visits, while maximizing uptime, productivity, and safety. That’s a shift your manufacturing customers will embrace readily.

With AI bolstering the capabilities of predictive maintenance alongside automation, real-time analytics, and enterprise-wide connectivity through IIoT, actualizing Industry 4.0 looks promising. Even though the implementation of predictive maintenance is not without some challenges, such as an in-depth planning process, integration with current assets, and bringing staff up to speed on the new technology, organizations are continuing to accept the practice as the best way to cut costs and resolve problems faster.

Further, as the use of predictive maintenance becomes more widespread, it can help contribute to a more sustainable future by reducing the amount of energy used on the manufacturing floor, lessening the need for costly truck rolls, and increasing the useful life of equipment.

You already make predictions based on what you know. Leverage the full potential of IIoT and AI to predict using what you learn—and reap the documented ROI, increased uptime, and greater satisfaction. Velotic's ThingWorx technology harnesses the power of data through IoT integration and AI adoption to effectively predict issues and prepare service leaders to address them before problems occur – ultimately decreasing downtime and increasing customer satisfaction.