The Industrial Internet of Things (IIoT) has revolutionised every industry across the globe, enabling real-time data collection and analysis to optimise operations.

At the core of IIoT technologies is machine learning (ML), a subset of artificial intelligence (AI) that enables machines to process and learn from data, identify patterns and make predictions.

“Sophisticated algorithms are transforming machine learning and ultimately IIoT - particularly when it comes to condition-based monitoring. This helps manufacturers minimise equipment downtime and enhance productivity,” says Supriya Suhale, Category Manager – Connectivity for Beckhoff and spokesperson for Open IIoT,  an industry group comprising of leading automation brands SMC Corporation Australia & New Zealand, Nord Drivesystems, Beckhoff Australia and Balluff.

Suhale believes that while its benefits are sometimes overlooked, ML addresses one of the biggest challenges in IIoT: managing a vast amount of data.

“In IIoT, data is the name of the game,” she says. 

“ML can collect and process data from various sources such as sensors, cameras and temperature readings. It helps categorise data in terms of relevancy, providing clear insights that enable decision making.”

Furthermore, she adds that ML identifies meaningful trends and patterns, enabling manufacturers to focus on actionable insights rather than getting lost in irrelevant data points.

“Once raw data is transformed, it becomes a valuable asset that can inform decision making and optimise industrial processes,” says Suhale.

One of the most impactful applications of ML in IIoT lies in the area of predictive maintenance through condition monitoring systems.

“Downtime is one of the biggest costs that manufacturers incur, equating to USD 50 billion each year (Forbes),” explains Suhale.

 

 

For this reason, predictive maintenance has been widely deployed as a preventative measure to anticipate when equipment is likely to fail or requires a service.

“Condition monitoring systems allow operators to schedule maintenance before a breakdown occurs and to plan more efficiently for regular equipment services,” she says. 

“By analysing data from machine sensors and other inputs, machine learning predicts when a piece of equipment will need maintenance and can estimate – and extend - its remaining lifespan.”

Suhale highlights several key applications used to predict potential equipment issues as follows:

 

⊗ Vibration Analysis: Machine learning algorithms can diagnose faults by analysing vibration patterns in machinery.

⊗ Oil Analysis: AI models detect changes in viscosity, helping predict equipment failures related to lubrication issues. 

⊗ Ultrasonic Monitoring: Machine learning improves leak detection by analysing ultrasonic signals, ensuring more accurate identification of small but costly problems.

⊗ Temperature Monitoring: AI can analyse temperature data to prevent overheating, prolonging the life of equipment.

 

“These examples illustrate the wide range of machine learning applications in condition monitoring.

By integrating data from different sensors and inputs, machine learning can provide a more comprehensive view of equipment health.”

Suhale shares that as industries continue to adopt IIoT technologies, the benefits of continuous monitoring powered by ML are becoming clearer. 

“AI can take the insights gained from past data and improve condition monitoring over time,” she says. 

“This helps not only in identifying potential faults but also in optimising maintenance schedules and strategies.”

 

 

In addition to enhancing equipment maintenance, machine learning provides insights into the effectiveness of current maintenance plans, offering suggestions for improvement.

“Ultimately, ML in IIoT enables manufacturers to make smarter, data-driven decisions, which leads to better outcomes in terms of efficiency, safety, productivity and most importantly, energy savings,” says Suhale.

Interesting to note is that energy efficiency can also be monitored and adjusted. 

“ML can identify anomalies and inefficiencies in operations, such as the under or over utilisation of energy and fluctuating load demands,” says Suhale.

“Based on these outcomes it can recommend adjustments during low- demand periods (idling time), recommend efficient process sequences and boost energy management and efficiencies.”

Looking ahead, Suhale says that the continuous evolution of ML ensures that IIoT systems will stay ahead of equipment issues, driving operational efficiency while minimising risks. 

“As these technologies advance, industries will see further improvements in the way they manage equipment and resources.”

 

LeackStat 2024