How IoT and predictive maintenance data analytics could dramatically improve your equipment management

Aug 5 2021 | General

Predictive maintenance is not new. Machine operators and mechanics have been monitoring the state of their equipment since machines were invented. Some could even tell you that a motor would soon fail just by touching the equipment.

Since then, IoT (also known as the ‘Internet of Things’) and data analytics have changed the game. Wireless sensors use live data to analyse and forecast the health of entire fleets. Say goodbye to manual equipment checks. 

This easy-to-use technology has recently become more affordable – even for the smallest industry players. Read on to learn how your company could use predictive maintenance data analytics to dramatically improve equipment management. 

First, let’s take more of a look into exactly what predictive maintenance is. 

 

What is predictive maintenance?

The goal of predictive maintenance is to find and address failures before they happen. You can forecast when your equipment might fail by looking at the condition of each piece. Your maintenance team can then swing into action, instead of waiting for something to break. 

 

Predictive maintenance can be more cost effective than preventive maintenance

If you’re using preventive maintenance now to manage your equipment, you might wonder what sets predictive maintenance apart. The key difference is that predictive maintenance maintains equipment as needed, compared to happening on a routine schedule.

A downside of preventive maintenance is that it assumes all assets in the same class require the same maintenance regime. In reality, some assets are used more than others, making them more likely to fail before their next scheduled maintenance. On the other hand, some assets could be functioning well and not need their next scheduled maintenance.  

Predictive maintenance can be more efficient than preventive maintenance, which often makes it more cost-effective. In fact, researchers have estimated that predictive maintenance programs can result in savings of 8-12%, compared to programs relying solely on preventative maintenance.

On average, a functional predictive maintenance program in an industrial setting can result in these savings:

  • return on investment: 10 times
  • reduction in maintenance costs: 25% to 30%
  • elimination of breakdowns: 70% to 75%
  • reduction in downtime: 35% to 45%
  • increase in production: 20% to 25%.

 

IoT and data analytics take predictive maintenance to the next level

Predictive maintenance really shines when coupled with IoT and data analytics. IoT is a way of describing a network of devices connected wirelessly so they can transfer data and ‘talk’. Industrial IoT predictive maintenance typically involves a network of wireless sensors attached to individual asset items. The sensors send live, continuous data to the cloud or a bank of servers. 

In essence, IoT technology makes it possible to remotely monitor equipment in real time. For construction, it means real-time alerts that the generator you’ve got on dry-hire at one of your job sites is about to fail. When managing construction equipment rentals, you’ll have greater visibility of where your assets are and what condition they’re in at all times. You will be able to increase your rental turnaround times and reduce costly on-site equipment failures, increasing customer satisfaction and loyalty.

IoT sensors create the data, but it still needs to be analysed. AI and machine learning powers predictive maintenance data analytics.  You can use data analytic algorithms to predict the remaining useful life of an asset, identify any abnormal machine behaviour and trigger suitable corrective actions.

IoT and predictive maintenance data analytics are a winning combination because they can significantly improve efficiency and increase equipment uptime. This means your company saves time and money. 

 

How to get smart data rather than big data

The sheer amount of data IoT produces can be overwhelming. After all, it’s no use having the predictive maintenance data available if you can’t make sense of it. Instead of just having big data, it’s about having smart data. That’s where data fusion asset monitoring comes in. It translates all your equipment data into a single, easy-to-understand narrative so your team can quickly make good maintenance decisions. 

Of course, moving your company to a new maintenance strategy doesn’t come without costs. Predictive maintenance programs based on IoT and data analytics involve upfront implementation costs. 

However, you can take advantage of the latest IoT technology to implement predictive maintenance at your own pace.  For example, magnets can place new sensors on your equipment, meaning no downtime for installation. You can also focus your data collection efforts on critical machinery or asset items that need servicing more often. 

Data fusion makes your data smarter, giving you more bang for your buck when it comes to implementing advanced predictive maintenance programs. Data fusion can send the relevant data to the right people across your company. For example, you can send data to:

  • maintenance teams to prevent failures before they happen
  • logistics and procurement teams so they can better manage warehousing and spare parts
  • human resource teams to improve risk monitoring and safety procedures
  • designers to improve future product designs
  • management to streamline processes and identify potential new business opportunities.

 

There’s more to smart data than sensors

While data from equipment sensors is incredibly valuable, it doesn’t give you the complete picture of an asset’s life cycle. For example, sensor data could tell you that the temperature of an excavator on your construction site is fluctuating wildly. You might suspect operator error. But when you combine sensor data with other data sources, like whether the operator is approved to use the excavator, you can make better maintenance decisions. 

That’s the beauty of our data fusion platform, Perspio™. It works by ingesting and integrating data from multiple field data points, back-end applications and third-party sources, before fusing it to generate contextual insights.

The platform feeds this rich data back into your enterprise applications and asset management systems. It can also present it to relevant users through advanced reporting.

 

Want to use IoT and predictive maintenance data analytics but don’t know where to start?


From logistics to equipment rental and construction, every operation can enhance or optimise their processes. This means there could be significant benefits of using IoT and predictive maintenance data analytics in your industry.

You don’t even need to have existing devices or connected assets to take advantage of Perspio™. It can access and ingest data from any type of field source you have, as well as relevant, publicly available third-party sources. Perspio™’s reporting functionality means you still benefit from role-based reporting and contextual insights from these sources, even if the platform doesn’t connect to any back-end applications. 

When you are ready to add devices and connected assets, our team can help you source and connect them.

Get in touch today to find out how timely data and recommendations can improve your company’s operational efficiency.