Machine learning (ML) is becoming a crucial element of the Industrial Internet of Things (IIoT), assisting manufacturers in optimising equipment maintenance and reducing downtime. Supriya Suhale, category manager – connectivity at Beckhoff and spokesperson for Open IIoT expounds ML's merits.
According to Suhale, ML is particularly useful for condition-based monitoring systems, helping to extend equipment lifespan and improve productivity.
Suhale explains that ML collects data from various sources, such as sensors and temperature readings, and helps categorise it based on relevancy. “In IIoT, data is the name of the game,” she says. “ML enables manufacturers to focus on actionable insights rather than irrelevant data points.”
A primary application of ML in IIoT is predictive maintenance, which helps manufacturers anticipate equipment failures. By analysing data from machine sensors, ML can estimate when equipment will need servicing and potentially extend its remaining lifespan. Suhale notes that downtime is a significant cost for manufacturers, amounting to USD 50 billion each year.
Key applications of ML in condition monitoring include vibration analysis, oil analysis, ultrasonic monitoring, and temperature monitoring, all of which provide valuable insights into equipment health. For example, vibration patterns in machinery can reveal faults, while temperature data can prevent overheating, thus prolonging equipment’s life.
Suhale highlights the energy-saving potential of ML, noting that it can identify inefficiencies in operations, such as over-utilisation of energy during idle periods. “ML can recommend adjustments based on these insights, improving energy management and operational efficiency,” she adds.
As IIoT technologies continue to evolve, Suhale expects ML to play an increasingly critical role in equipment management and resource optimisation. Continuous monitoring powered by ML not only improves maintenance schedules but also enhances overall operational efficiency, making it a valuable tool for manufacturers looking to reduce risks and improve productivity.
