Low- or No-Maintenance Industrial Machinery

The average cost of equipment downtime is now $260,000 per hour. Whether downtime stems from scheduled maintenance or results from unexpected problems, the price remains the same — and climbs rapidly as downtime drags on.

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While it’s impossible to entirely avoid downtime, companies can limit operational impact by adopting technologies that minimize maintenance requirements. 

The Operational Impacts of Downtime

Companies now spend an average of 19 hours per week on scheduled maintenance, and just under half of businesses surveyed said they spent between 21% and 40% of their operating budgets on equipment cleaning and maintenance.

And that’s if everything goes to plan; industrial manufacturers now see up to $50 billion each year in unplanned downtime costs. The common culprits? Outdated or poorly maintained industrial machinery.

In some cases, older machinery simply can’t handle the production demands of growing businesses, leading to component failures or excessive wear and tear. In others, companies lack visibility into operations data which indicates the need for proactive maintenance.

The result? Significant costs that rapidly accumulate the longer systems are down. 

Making the Move to Minimal Maintenance: Four Key Technologies

If businesses can proactively predict potential problems, they can reduce maintenance needs and limit overall downtime. Accomplishing this goal requires the integration of new technologies, including:

Advanced control systems

The more data businesses have about machinery operations, the better equipped they are to take action and address critical issues. Using advanced control systems that include programmable logic controllers (PLCs) and connect with industrial Internet of Things (IIoT) sensors, companies can track equipment performance over time and reduce downtime through predictive maintenance.

For example, control systems monitoring a piece of high-volume machinery might detect a small but consistent rise in operating temperature, which could indicate increased friction in key components. By replacing these components before they fail, the business saves money by avoiding costly downtime.

Artificial intelligence frameworks

Advanced AI frameworks can help companies detect problematic patterns in machinery that may not be otherwise apparent. Designed to learn over time, these frameworks collect data about production line operations to establish a performance baseline and then evaluate ongoing productivity against this data. If expected and actual results don’t match, AI tools can notify staff that maintenance is required.

Augmented reality tools

Operating screens and interfaces underpinned by augmented reality (AR) tools allow staff to “see” how machinery is performing rather than simply reading data outputs. Consider an AR control panel that uses multiple infrared cameras to record temperature values across complex machinery.

If these values were reported as simple data points, users might miss small outliers and assume maintenance was not required. Using AR control panels to “view” this data as a real-time heat map, meanwhile, teams are better equipped to pinpoint problem areas and schedule proactive maintenance.

Automated sensor solutions

One common challenge in reducing the need for machinery maintenance is the impact of sensor technologies on the machines themselves.

Consider a flow sensor placed inside a pipe. The sensor itself not only impacts the flow of liquid through the pipe but is also impacted by this flow. This creates a dual problem: Parts may wear more quickly when physical sensors are added, and the sensors themselves may start to fail.

Automated sensor solutions help solve this problem. For example, clamp-on ultrasonic flow meters measure the velocity of fluid flowing through a pipe but do so without impacting internal operations. They’re also free of moving parts, in turn reducing their need for maintenance.

Staying the Course

All machinery requires maintenance. The more maintenance required, however, the more expensive operations become. By implementing solutions such as advanced control systems, AI frameworks, AR tools and automated sensors, companies can minimize maintenance requirements and reduce the risk of unexpected downtime.

Author bio

Izzy Rivera is the HVAC & Gas Service Manager at Emerson. Rivera has been involved with ultrasonic flow measurement for 40 years, spanning the history and development of this technology. He was involved in developing the first fully integrated ultrasonic gas meter. He co-founded FLEXIM AMERICAS back in 2005, which is now a part of Emerson Electric.