Predictive Maintenance & Machine Learning: A Complete Guide

A technician using a tablet within a factory line, presumably in an IoT predictive maintenance machine learning application.

Predictive maintenance technology detects potential equipment failures before they occur, avoiding or minimizing downtime. Machine learning is a powerful tool that analyzes data, learns, and creates increasingly accurate predictions. Together, predictive maintenance and machine learning will improve the reliability of an organization’s maintenance and asset management strategy. 

Below, we’ll discuss the basics of predictive maintenance and machine learning and how each technology enhances the other.

Predictive Maintenance and the Intelligent Facility

According to Plant Engineering, 40% of today’s facilities employ predictive maintenance to reduce downtime, improve ROI, and increase the overall longevity and efficiency of assets. However, over 50% of facilities continue to use reactive maintenance strategies, and even more use preventive or proactive maintenance.

What Is Predictive Maintenance?

Predictive maintenance denotes a condition-based maintenance approach that uses data analytics to predict when an asset will fail or need repair. To do so, it leverages the Internet of Things (IoT), a network of physical objects connected to the internet.

IoT sensors collect machine-level data about an asset’s performance in real time. But this data still has to be analyzed, and then actions, the correct actions, have to take place to remediate and prevent costly downtime. Many organizations use intelligent, predictive maintenance machine learning to analyze the collected data.

An old-fashioned sensor on a machine

What Are the Benefits of Predictive Maintenance?

Predictive maintenance is important because it can prevent downtime, improve safety, and reduce maintenance costs. Rather than taking a reactive approach to system maintenance, a facility can plan for it in advance.

Predictive maintenance can be used to schedule repairs during regularly scheduled downtime, further reducing the impact of repairs on production. Additionally, predictive maintenance yields better, more accurate insights into an organization’s assets and asset conditions.

How Does Predictive Maintenance Work?

Predictive maintenance uses sensors installed on an asset to collect information and metrics, such as temperature and vibration. This data is then analyzed to predict when the machine will need to be repaired or replaced.

A screenshot of LLumin’s software system.

However, this requires accurate and reliable data analysis, as well as optimized hardware and software. Maintenance technicians employ an interface—an all-in-one dashboard to review equipment status and dig deeper into equipment issues.

Predictive Maintenance and Machine Learning

Machine learning is a subset of artificial intelligence (AI) that analyzes data and learns from it. By harnessing machine learning, organizations can make increasingly accurate predictions of when machine failure will occur. In this way, the overall accuracy of predictive maintenance can also be enhanced. 

What Is Machine Learning?

Across many industries and applications, machine learning algorithms are used to detect patterns in data automatically. These patterns can be used to make predictions about future events. For example, a machine learning algorithm could predict when a particular asset will fail based on historical information and data collected from its sensors. 

Machine learning isn’t true AI, though. It cannot make intelligent decisions, but it can provide maintenance technicians with the information they need to make the best decisions for the facility.

What Are the Benefits of Using Machine Learning for Predictive Maintenance?

Without machine learning, companies need to create predictive maintenance schedules based on algorithms. Machine learning gets to “know” the systems and adapts to the technology over time. For example, a regular non-intelligent algorithm could indicate that a machine is reaching failure because its internal temperature is 180℉. But an intelligent algorithm might “realize” that the assets within the system only reach failure at 190℉ due to other environmental factors.

How Can Machine Learning Be Used for Predictive Maintenance?

Predictive maintenance solutions collect sensor-driven data from assets that is then fed into the machine learning platform. The machine learning platform analyzes the data and looks for patterns that indicate when an asset is likely to fail. Importantly, these predictions become more accurate over time as the system learns and grows.

The Future of Predictive Maintenance and Machine Learning

Predictive maintenance and machine learning are important technologies that can help organizations improve the efficiency of their assets. And as machine learning technology becomes more advanced, it becomes easier for organizations to use the data they collect. 

As predictive maintenance and machine learning evolve together, even more granular control over sensors will be available, as well as more accurate sensors and better real-time reports. Organizations, as a result, will be able to react swiftly to changes in assets to reduce or eliminate disruptions.

Still, an organization needs the right system to support predictive maintenance and machine learning. LLumin’s CMMS+ is an all-in-one predictive maintenance platform and machine learning solution—with implementation support for your digital transformation.

If you want to benefit from best-in-class predictive maintenance machine learning technologies and all the advantages of sensor-driven predictive maintenance, we can help.

Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track assets for industrial plants, municipalities, utilities, fleets, and facilities. To learn more about predictive maintenance and machine learning and how they can help you reduce downtime, we encourage you to schedule a free demo or contact the experts at LLumin and see how our CMMS+ software can help you reach your efficiency and cost-cutting goals.