Measuring the Cost of not Using IoT for Predictive Maintenance

Farnell - Measuring the cost of not using IoT for predictive maintenance

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Article written by: Eleanor Young, Category Director Industrial, Farnell

One of the major uses for IoT devices is the monitoring of production assets such as compressors and motors to ensure their continued operation.

By using frequently gathered data from connected monitoring devices such as strain gauges and vibration monitors, manufacturing companies can move to predictive maintenance, which is designed to assess the condition of a piece of equipment and determine when maintenance tasks need to be performed.

The method aims to predict how the device’s condition will change over time, based on statistical techniques. Ultimately the goal is to conduct maintenance on the asset at the most cost-effective time, ensuring it continues to perform as required, while avoiding too many production interruptions.

Predictive maintenance gives insights into the machines and component that are likely to fail and when. This allows maintenance staff to investigate the machine’s condition, conduct maintenance to fit in with production and carry out any repairs before the machine fails.

This contrasts with preventive maintenance. As a method based on pre-set maintenance intervals that pay little attention to the actual health of the machine, this can be disruptive and thus lead to losses.

A preventive maintenance philosophy also runs the risk of parts being replaced before they need to be, leading to extra costs. Other dangers include parts not being correctly replaced or components becoming misaligned. Although preventive maintenance can be easier to plan, it can also lead to using more time, parts, and money than necessary.

Online analysis keeps a weather eye on wear

One of the key uses of IoT enabled devices is in monitoring the vibrations of machines such as motors. By analysing these vibrations, engineers can gain more information about the condition of the machine and about when it might fail. This allows them to carry out maintenance to prevent the possible failure and extend the machine’s working life.

Some industries are particularly reliant on rotating machinery and the equipment they drive, such as pumps and compressors. As these are driven by motors, they can frequently be the cause of unplanned downtime due to failures if not maintained correctly.

A major cause of these failures is wear and misalignment in components such as bearing races, gear wheels and shafts, often caused by vibrations. Analysing and understanding these vibrations is the basis of predictive maintenance.

Accelerometers are a popular tool for predictive maintenance and work by measuring the vibrations that cause the accelerometer to produce an electrical signal, which can be processed to produce useable vibration data.

Piezoelectric accelerometers are also widely deployed as they produce a strong, clear signal at most frequencies, while other common sensors are strain gauges and microphone sensors. Strain gauges detect vibrations by measuring the time taken for an electric current to pass through a grid that is deformed by movement.

Invest in IoT to cut the cost of operations

A predictive maintenance programme based on data from IoT sensors can cut costs significantly by dramatically reducing or even eliminating the unplanned downtime that can result from machine failures. It can also aid cost-effective operations by ensuring that maintenance staff are deployed in the best way. Perhaps the main benefit is increasing the amount of time that machines have to do useful work, boosting production capacity.

These benefits can be quite significant and can include: 

  • Maintenance costs – down by 50%
  • Unexpected failures – reduced by 55%
  • Repair and overhaul time – down by 60%
  • Spare parts inventory – reduced by 30%
  • Mean Time Between Failures (MTBF) – increased by 30%
  • Uptime – increased by 30% [1]

For a typical manufacturing plant, a 10% reduction in maintenance costs can produce the same financial benefit as a 40% increase in sales. [2]

Matt Dentino is the Industrial Internet of Things channel manager for Advantech in North America: “Our modules provide the backend intelligence to easily and quickly turn that data into insight and make it available for the top floor.

“You don’t need a person taking six weeks to determine your energy usage – now, those numbers are pulled together in minutes, if not seconds.”

The huge cost of not running

Production interruptions from failed equipment can cost many hundreds of thousands of pounds over just a short period of time.

For example, an analysis by ABB Motors outlined the potential costs that can arise from a motor failure. [3]

At an energy cost of 11p/kWh, and with the motor running for 8,400 hours per year, the cost of running a 315kW motor with a 95.5% efficiency over a 20-year lifetime would be £6,094,704. This is extremely high compared to the typical purchase cost of £18,000.

Yet the cost of not running the motor is also high. For a motor used in the oil and gas industry, such a failure could produce losses of £220,000 an hour. Just a single stoppage of ten hours over the motor’s 20year life span would thus lead to losses of £2,200,000.

Using a predictive maintenance system based on IoT data can save these costs.

Alexandra Rangel is a National PowerXpert Application Engineer from Eaton. In a recent interview with Farnell, she outlined the importance of data driven predictive maintenance: “Being able to get the different health parameters or get alarms from each component and bring it to a central location is vital,” says Rangel. “It allows people to prioritise a piece of equipment in the next maintenance round because something is not right, or alternatively to intervene before something happens.

“But the biggest thing is, how much would it cost if that tool were to go down and it stops your whole manufacturing floor? What if it stops your whole conveyor line? And, if that process is down for eight hours, how much are you losing by not being able to roll out products?”

It’s clear that the ability to gather machine data and make smart decisions about it is an increasingly important part of companies’ efforts to control costs and remain profitable. 


[1] Plant Engineer’s Handbook, R. Keith Mobley (2001)



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