IoT Predictive Maintenance

There are two schools of thought when it comes to equipment maintenance. The first approach is a common one: simply wait until a piece of equipment or machinery breaks and then make a repair to fix it. While this approach might work for some things in life, it is unrealistic for large-scale industrial organizations that rely on thousands or even millions of assets in their everyday operations.

A wiser strategy for such enterprises is to take a proactive approach to maintenance, which means making regular repairs to equipment to prevent failure and outages. However, equipment problems aren’t always obvious to the naked eye, meaning organizations may require assistance from technology or software to gain a better understanding of their assets’ performance and any potential issues that may arise. One such tool that can assist with this is IoT predictive maintenance.


What is IoT predictive maintenance?

IoT predictive maintenance is a maintenance strategy that involves using the Internet of Things to gather and analyze data about assets, equipment or machinery. Sensors and other instruments collect data about equipment status to detect any issues that may need to be addressed to prevent future outages and unnecessary downtime. 

Before we explain how IoT predictive maintenance works, you may be wondering what the Internet of Things is. Often abbreviated as “IoT,” the Internet of Things consists of various devices, software and systems connected to one another through the internet. These “things” can then transmit valuable information to each other to create a comprehensive and integrative network of powerful data. 

In IoT predictive maintenance, the Internet of Things often consists of sensors and monitors that are either placed on or built into equipment to monitor a wide range of variables that may indicate potential equipment issues. These instruments gather and transmit asset data to other “things” in the network, such as predictive maintenance software, CMMS software or other smart manufacturing systems. By gathering and transmitting equipment performance data in real time, other IoT technologies can run predictive maintenance analytics to identify any potential issues that may result in equipment failure. This process helps organizations better predict the chances of outages or other disruptions so that they can take a proactive approach to maintenance.


IoT predictive maintenance across industries

Various industries can minimize downtime and increase throughput by integrating IoT predictive maintenance with their maintenance strategies. Some examples of IoT predictive maintenance within specific industries include:

  • Pharmaceutical - Pharmaceutical products need to be stored at a specific temperature to maintain their integrity. Using refrigeration sensors to connect to predictive maintenance software helps pharmaceutical manufacturers detect signs of equipment malfunction and act accordingly.

  • Utilities - Utility companies can implement IoT predictive maintenance by taking advantage of predictive maintenance tools to prevent power outages. Predictive maintenance software can integrate with artificial intelligence and sensor data to identify precipitating factors that contribute to outages. The software can then determine the best maintenance plan to prevent future outages.

  • Transportation - Failure of any sort of fleet vehicle equipment can result in time-consuming and costly repairs. By using IoT predictive maintenance, transportation enterprises can use sensor data to detect issues ahead of time and schedule maintenance accordingly.

  • Manufacturing - Using predictive maintenance tools such as infrared sensors helps product manufacturers monitor equipment temperature to prevent overheating, which may cause outages and unplanned downtime down the line. CMMS maintenance software can then integrate this data and create a maintenance plan to ensure continuous throughput.

Virtually any industry that depends on physical assets for production can take advantage of IoT predictive maintenance. By integrating real-time sensor data with powerful analytics, industrial enterprises can deepen their understanding of factors that contribute to asset failure and minimize future unplanned outages. 


Applying IoT predictive maintenance

Putting IoT predictive maintenance into practice may sound intimidating at first. However, there are some small steps enterprises can take to make the process a bit easier. We recommend starting small by choosing a “pilot” asset to begin integrating with predictive maintenance tools and software. Focusing on just one physical asset to start with can make the process feel less overwhelming and give you a better idea of whether IoT predictive maintenance is right for your business. 

Once you decide on a pilot asset, CMMS software and predictive maintenance tools, you can begin merging these things together to collect relevant asset performance data. Predictive maintenance software can then use machine learning and algorithms to assess the asset’s condition, estimate when a failure will occur, and determine an appropriate planned maintenance schedule. 

You will then want to continuously monitor and report on the asset’s performance to determine if the predictive maintenance strategy is delivering results. If it is, then you may want to consider expanding IoT predictive maintenance to other assets within your enterprise to continue enhancing productivity.

IoT predictive maintenance is a highly intelligent strategy that any organization can use to enhance its approach to asset maintenance. Integrating real-time data with predictive analytics, IoT predictive maintenance can help enterprises improve productivity, reduce the chances of unplanned downtime, and maximize asset performance throughout their lifecycle.



What is an example of predictive maintenance?
Predictive maintenance plays a role across various industries, including oil and gas, utilities, manufacturing, and transportation. For example, predictive maintenance in the utilities industry may involve preventing power outages by using predictive maintenance software and data analytics for utilities.

What is IoT predictive maintenance?
IoT predictive maintenance involves using data gathered through IoT technology to assess equipment, assets or machinery and better predict potential failure or outages. Using IoT technology helps organizations gather real-time asset data they can use to determine appropriate maintenance strategies to prevent equipment downtime and increase throughput.

How does predictive maintenance work?
Predictive maintenance involves taking a proactive approach to maintenance as opposed to a reactive approach. Rather than waiting until a piece of machinery breaks down, predictive maintenance involves using tools such as IoT predictive maintenance technology and predictive maintenance software to gather equipment data in hopes of detecting issues that may result in problems down the road. After gathering this data, enterprises can run predictive maintenance analytics to predict equipment failure and determine the best course of action to mitigate this risk.