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, with many implementing predictive maintenance robotics. 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.

Challenges in Predictive Maintenance

Many companies have struggled to capture the full benefits of predictive maintenance programs. The reasons vary from organization to organization—including the loss of experienced staff, schedule limitations, and difficulties inspecting certain areas. And, 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.

The Role of AI in Predictive Maintenance

One of the major advantages of a predictive maintenance program is that it helps replicate at scale the intuition that experienced maintenance professionals deliver. An experienced engineer can hear when a machine sounds off or notice if equipment seems a little less efficient than normal. But, 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. Machine learning (ML) algorithms help detect anomalies before they lead to factory outages—a key factor in the predictive nature of predictive maintenance. AI-enabled software can trigger alerts for maintenance teams or 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.

Collecting the Right Data for Asset Optimization

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 detection to find bearing failures. 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.

Spot equipped for thermal inspections, at the top of stairs in an industrial setting

Turning up the heat on predictive maintenance

For example, detecting an overheating pump or motor before it fails can mean the difference between a simple repair and tens of thousands of dollars per hour in downtime. 

But many factories only have a fraction of their assets equipped with fixed thermal sensors. Either a skilled thermographer takes time out of their busy day to check hundreds of motors and moving parts, or you hope that the motor that fails is the one with the fixed sensor. Spotting thermal anomalies before failure can be like finding a needle in a haystack. 

Automating inspections with the Spot® robot offers another option. These robots walk through a facility autonomously, gathering the thermal imagery you need. They offer a repeatable, scalable method for achieving your predictive maintenance goals and taking the next step toward Industry 4.0.

Agile Mobile Robots Accelerate Maintenance Programs

Agile mobile robots like Spot act as roving IoT platforms, autonomously collecting the right data at the right time for effective predictive maintenance. 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.

Simply integrate your preferred sensing payloads—thermal cameras, pan-tilt-zoom (PTZ) cameras, acoustic sensors, laser scanners—and record inspection routes as autonomous missions to be performed on a set schedule. Spot uses intelligent sensor pointing to collect the same data the same way every time. Additionally, Spot can operate in dangerous places in your facility without requiring a shutdown—reducing downtime and improving safety for people on site.

Spot doesn’t just deliver the comprehensive, structured data that your facility needs for an effective predictive maintenance program. It enables you to reduce the burden on maintenance teams’ schedules and budgets, allowing technicians to focus on true corrective work rather than inspections.

An advanced inspection solution

We offer a full set of tools to deploy Spot as a thermal inspection solution, analyze the results, and deliver value for your predictive maintenance program—including sensing and compute payloads, software, and training and support resources.

Fleet Management: Use Orbit fleet management software to manage your Spot robots, the missions they perform, and the data they capture. With site maps, mission editing, and scheduling features, it’s never been easier to manage robots in the context of your facility.

Alerts: Spot can also trigger email or text alerts to notify operators of any anomalies it detects. Your maintenance team can quickly surface critical issues and group, triage, and dismiss alerts in a single click in Orbit.

Trends: Orbit uses the structured data captured by Spot to generate visualizations and make it easier to understand trends of time.

Integrations: Orbit includes an API that enables external systems—like a facility’s existing asset management platform—to query inspection data or automate downstream processes.

Training & Support: We offer a range of resources to help you get the most out of your solution including training, customer support, and robust documentation.

Using Spot for Real World Applications

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.

Many companies are already using Spot as part of their journey to predictive maintenance and Industry 4.0. The robot is working in a variety of facilities, performing automated thermal inspections of pumps, motors, bushings, insulators, steam traps, and conveyors. And that’s helping maintenance teams identify simple repairs before they become expensive problems.

To learn more about getting started with agile mobile robots for industrial inspection, watch our on-demand webinar on the future of asset management and hear from our experts how Spot is making industrial inspections simple, efficient, and cost-effective in our webinar on elevating your predictive maintenance with automated thermal inspections.