Effective maintenance programs are vital in manufacturing, energy and utilities, and other asset-intensive industries—preventing equipment failures that lead to costly downtime and repairs. Smart organizations have shifted toward predictive maintenance strategies. Instead of periodically checking assets and replacing parts on fixed schedules, companies are now using artificial intelligence (AI) to identify warning signs of impending issues before they cause damage or outages. This approach can help extend asset lifespans, extract the full value of equipment and parts, and avoid unplanned outages.

Of course, predictive maintenance comes with its own set of challenges. First, these programs require more frequent data about your assets. If you want to know when a machine starts running hot or producing an abnormal noise—signs that indicate maintenance is needed—you need to check the machines once a week or even once a day, rather than following the quarterly or yearly schedules used in preventative maintenance. This asset condition monitoring can take a lot of work up front to ensure that you’re measuring the right things at the right time. Manual inspection and documentation is time and resource-intensive. Installing fixed sensors can be costly and may still leave gaps in your data. Areas of your facility may be hard to access during normal operations.

But if you can overcome these challenges, predictive maintenance offers significant ROI, reducing costs from both equipment maintenance and downtime. And now, new technologies are making it easier than ever to automate data capture, process data in real-time, and ensure asset optimization.


One of the major advantages of a predictive maintenance program is that it helps replicate the intuitive approach that many maintenance professionals bring to their work at scale. An experienced engineer can hear when a machine sounds off or notice when pressure gauges seem to be reading a little higher than normal. Unfortunately, this institutional knowledge can be lost when employees retire or move onto new roles, and even the best maintenance teams can’t be everywhere all the time.

AI systems help fill the gap, processing data on asset conditions through computer vision (CV) or other data-driven machine learning models. These algorithms help detect anomalies before they lead to factory outages—a key factor in the predictive nature of predictive maintenance. AI models can trigger alerts for maintenance teams to assess these anomalies or even integrate with Enterprise Asset Management (EAM) systems to automatically generate work orders. 

The goal of automation and AI in predictive maintenance isn’t to replace the institutional knowledge and expertise that maintenance managers and engineers bring to the table. It’s to simplify the amount of manual work required for them to make maintenance programs successful. With many organizations facing both labor shortages and increased production demand, it’s increasingly important to amplify maintenance teams’ resources, so they can spend less time on data capture and basic analysis, and more time solving complex problems and applying their expertise.


There are two main data challenges for predictive maintenance. First is identifying the key indicators for each asset you’re monitoring. And second is collecting structured consistent data that AI systems can use effectively.

The type of data you need for asset condition monitoring depends on the asset. A conveyor system might require thermal images to see if the motor is running hot. A compressor might need an acoustic sensor to detect air leaks. A pump might require vibration monitoring to detect irregular operations. Of course, these aren’t mutually exclusive; you may want to perform both acoustic and thermal inspections of the same machine. And, across an entire facility, you are likely to have multiple assets with different parameters. The scale and diversity of this data is one reason trying to sensorize every asset is impractical and costly.

Additionally, AI systems require consistent data to produce the most effective results—think the same image taken at the angle with the same lighting conditions every time. Data gaps—periods where data isn’t captured or fed into the predictive maintenance system—or data inconsistencies—minor variations from manual inspections, for example—introduce noise in the data and make it harder to spot real anomalies.

Automating data capture and condition monitoring enables your inspections to be completed the same way every time. Modern agile alternatives to fixed sensors help capture the data you need without costly installations or resource-intensive manual rounds.


Agile mobile robots like Spot act as roving IoT sensing platforms, performing autonomous rounds and readings to collect the right data at the right time for effective predictive maintenance. Equipped with sensing payloads, a single robot can reliably and repeatedly collect multiple types of data from assets in different locations around your facility, processing the data on the robot in real-time or integrating directly with an EAM system to streamline predictive maintenance processes.

Unlike traditional automation tools like fixed sensors or wheeled robots, Spot can easily move throughout your facility as needed, autonomously navigating stairs, cramped quarters, and areas hazardous for people. This approach offers the flexibility of a manual inspection with the repeatability of fixed sensors. Simply integrate your preferred sensing payloads—thermal cameras, pan-tilt-zoom (PTZ) cameras, acoustic sensors, laser scanners, gas sensors—and record asset condition monitoring routes as autonomous missions to be performed on a set schedule.

In addition to simplifying AI-driven predictive maintenance programs, this dynamic approach to data capture offers a range of other benefits. For example, if a potential safety hazard is detected, the same robot can be remotely operated by a maintenance expert to get a closer look at the asset in question and assess next steps. Similarly, you could monitor the condition of a specific defect detected on an asset to determine whether it can continue to be used between planned maintenance periods. An agile mobile robot is adaptable enough to serve a wide variety of maintenance needs.

To learn more about getting started with agile mobile robots for industrial inspection and asset management, watch our on-demand webinar on the future of asset management.