IoT and Predictive Maintenance: How Connected Sensors Help Teams Reduce Downtime

IoT and predictive maintenance give your team a real-time view of plant equipment conditions by using connected sensors to collect vibration, temperature, acoustic, and electrical data, then using analyses to turn these into alerts so teams know where to focus their work. This guide explains what IoT means in predictive maintenance, which sensors and technologies make it work, and how to choose a solution that fits your operation.

Every maintenance team makes the same call again and again: which asset gets attention first. IoT sensors help make calls using live data from the equipment, so your team decides with evidence from the machine’s condition.

What does IoT mean in predictive maintenance?

IoT stands for the Internet of Things; in predictive maintenance, it refers to a network of interconnected sensors, devices, and software that collect, transmit, and analyze real-time data about equipment condition on the floor. Simply put, IoT is a technology that connects physical equipment to the Internet so it can share data. 

IoT in predictive maintenance enables maintenance teams to spot issues early, schedule repairs around production, and prioritize work by equipment condition. IoT maintenance can help to reduce unplanned downtime with fewer emergencies, and often costly repairs.

In maintenance applications, IoT provides manufacturers with a smart, connected network that:

• Upgrades traditional reactive maintenance approaches with planned work based on actual asset conditions 
• Provides real-time visibility into equipment performance, even if you have assets in hazardous areas or ultra-low-RPM machines 
• Enables data-driven maintenance strategies, such as predictive maintenance and prescriptive maintenance
• Can connect previously isolated industrial systems, like bringing machine condition data to computerized maintenance management system (CMMS) and enterprise asset management (EAM) platforms your team already uses 
• Gives teams one place to track equipment health across your facilities

You may also see the term IIoT in your research. This stands for Industrial Internet of Things, which refers to the use of IoT technology specifically in the context of industrial manufacturing.

How does IoT help teams transition from preventive to predictive maintenance?

IoT provides teams with valuable live condition data, enabling them to shift from a calendar-based preventive maintenance approach to a predictive maintenance approach. With IoT, teams can see how every asset is actually performing. Depending on what the sensor data reveals, teams may prioritize or deprioritize servicing for assets, which results in more optimized maintenance programs. 

First, it’s important to understand that preventive and predictive maintenance are two different methodologies. Preventive maintenance programs rely on time-based schedules or usage values. Predictive maintenance programs use actual condition data to determine which assets need attention, which are running smoothly, and help teams to prioritize their work around machine performance indicators. 

Both approaches are more useful than reactive maintenance, which essentially equates to plant floor firefighting. According to Deloitte, “predictive maintenance increases productivity by 25%,

reduces breakdowns by 70% and lowers maintenance costs by 25%”  compared with reactive maintenance programs.

IoT can help regardless of whether you use a preventive or predictive maintenance approach in your facilities.

• IoT for preventive maintenance: A rising vibration or temperature trend can help teams determine if they need to move up a service visit. Stable readings let your team know they can extend the maintenance interval, making them available for any more pressing situations. IoT data helps teams build more accurate and efficient maintenance schedules.
• IoT for predictive maintenance: Live sensor data from IoT devices helps teams see how each asset is performing, making it easier to understand which assets need attention. Paired with AI diagnostics, IoT data can also identify the specific fault, explain what’s causing it, and recommend the next step.  

In truth, both these maintenance approaches complement each other. IoT is the bridge between them. Live condition data from IoT devices inform better preventive schedules, which, over time, evolve into condition-based predictive approaches. 

IoT sensors for predictive maintenance provide the connectivity required to capture data. Diagnostics or analytics tools help make collected data actionable. 

Let’s take a closer look at IoT in the context of predictive maintenance, since that’s where most manufacturing teams are heading.

How does IIoT help maintenance teams in industrial operations?

Industrial IoT (IIoT) helps maintenance teams plan their work around true equipment conditions instead of just reacting when failures occur. Data from IIoT sensors pairs with historical asset data to alert teams when a reading veers from normal, as well as provide a recommended repair plan. By alerting on developing faults, teams can get ahead of issues before they turn into unexpected failures, emergency repairs, or production interruptions.

While these benefits are certainly seen in your margins, they’re felt by the teams working on the plant floor. A Maintenance Technician at a prominent Woods Product Company stated that “Augury’s platform has become as important to me as my wrench or drill.” Essentially, technology is an integrated part of the operational day-to-day instead of a software line item in your budget that’s never used to inform production.

Best of all, the same data coming from IoT sensors can inform role-specific alerts for different members of your team. Here are some of the ways IoT helps specific maintenance team roles: 

Planners

• Plan repairs around production, not emergencies. The earlier the warning, the more time you have to order the right parts, schedule the crew, and insert the repair into scheduled stoppage time. Fortune Brands’ Fiberon plant used this approach to schedule a repair during a planned shutdown, saving $56,000 by avoiding potential unplanned downtime around an issue. These better-planned repair benefits snowballed, and over eight months, the plant avoided 178 hours of downtime and achieved 2.5x ROI.
• Line up parts and work orders before the job starts. Long part replacement lead times can turn a costly downtime into something astronomically expensive. Early AI alerts enable you to order anything you need before failure forces the issue. One Augury customer avoided $2.4 million in downtime and repair costs because they got the right spare part ordered in time when a single motor fault was detected early. Advanced machine alerts can also trigger work orders for you in your CMMS or EAM platform.

Reliability leaders

• Act on trustworthy alerts instead of false positives. Alerts are most useful when your teams are willing to act upon them. It’s also important your team gains trust in the system, which is why early wins are important. This was the case at Nestlé Purina’s Hartwell, Georgia plant. The team cut active alarms from more than 30 to single digits across 90 monitored assets, driving them to double the number of assets covered to 180. Since then, the plant has avoided more than $830,000 in costs and 100 hours of downtime.
• Demonstrate the program’s value and scale it. Condition history helps you see which assets improved and where to expand next by tracking the hours and costs you avoid by getting early alerts about issues. Using Augury’s AI, Purina’s maintenance team moved away from reactive firefighting and avoided $11 million in costs and 277 hours of unplanned downtime in 2024 alone.

Technicians

• Keep priorities in order and save time. When multiple issues arise at the same time, it can be difficult to properly prioritize what to fix first. Alternatively, you might end up sending technicians out to inspect healthy machines, which is a waste of resources that are already at their limit. IoT data can help to minimize unplanned downtime and ensure teams spend time investigating and fixing the right problems. Hill’s Pet Nutrition (a Colgate-Palmolive brand) used to rely on manual asset inspections and set maintenance schedules. They brought in Augury to better predict and respond to potential failures. A single blower failure ended up preventing the loss of nearly 500,000 pounds of product. 
• Know what’s happening and what to do next. Getting notified about an issue gets you barely halfway to solving it. AI can help teams know what needs fixing, how to fix it, and how urgent the issue is. And, because alerts are validated by CAT II/III certified experts, fewer, more accurate alerts are the norm. For one of Augury’s beverage manufacturing customers, the first IoT data transmission after sensor installation notified the team about bearing wear on a cooling water pump used in a vital process. Because the plant had redundancy, they took the machine offline, made the repair, and brought it back online without losing any production. This would not have been possible without Augury’s early diagnostic detection of a developing fault.

What other manufacturing teams benefit from IoT predictive maintenance?

In addition to maintenance teams, predictive maintenance programs using IoT also benefit plant directors, engineering leaders, and IT teams. The same asset condition data can be used by different roles to address different challenges. Plant directors see fewer unplanned stops. Engineering leads gain visibility into failure risk across multiple sites. IT teams get a connected system that meets security and integration requirements.

Many predictive maintenance benefits are felt plant-wide, not just isolated to maintenance teams. Forrester’s 2025 Total Economic Impact™ study on Augury’s Machine and Process Health solutions (which use industrial AI to help diagnose both issues and optimization opportunities) found that these condition-based IoT maintenance solutions helped the composite organization avoid 95 hours of unplanned downtime per site each year, worth $16.8 million over three years. 

How do IoT sensors support industrial predictive maintenance programs?

IoT sensors support predictive maintenance by measuring real-time signal shifts as equipment conditions change, particularly for vibration, temperature, magnetic flux, acoustic emissions, and electrical current. Each signal points to a different fault type (e.g., bearing wear, overheating, motor performance changes) that AI can flag weeks in advance, giving teams what they need to know for ordering parts and scheduling the crew before something breaks. 

Different sensor signals tell you different things, which in turn indicate specific faults: 

Vibration IoT sensor

• Measures: equipment oscillation patterns
• Points to: mechanical irregularities, bearing issues, alignment issues

Temperature IoT sensor

• Measures: thermal variations
• Points to: overheating components

Acoustic emission IoT sensor

• Measures: sound frequency changes
• Points to: early signs of mechanical wear, microscopic structural changes

Electrical current IoT sensor

• Measures: power consumption
• Points to: motor performance variations, electrical system issues

Best of all, many of these sensing signals can be collected through a single unit. This makes them simpler to install and maintain than using separate devices for each signal you want to monitor and track. For example, Augury’s Halo™ R4000 series captures triaxial vibration, temperature, and magnetic flux data in one sensor. AI combines the three signals to give you a fuller picture of the machine’s condition. Only one device is mounted on your asset, and your team is responsible for just one battery, which lasts up to five years.

For harsh, hazardous environments, Augury and Baker Hughes offer the Ranger Pro sensor and diagnostics, which measure vibration, temperature, and other signals in Class I/II and Division I/II areas.

How do IoT technologies work together in predictive maintenance?

In addition to sensors, IoT predictive maintenance uses wireless connectivity, edge computing, cloud computing, and analytics with machine learning. All these technologies work together to give you a complete real-time look at your operations. Sensors capture condition data, a gateway brings the data to edge and cloud computing platforms for processing, and AI analytics identify the patterns indicating a developing fault. Combined, your team gets guidance about which asset needs attention and what to do.

Here’s a more detailed look at how these IoT technologies work together to inform robust predictive maintenance programs: 

1. Sensors capture condition data. Mounted on your assets, sensors record signals (e.g., vibration; temperature) so teams have a continuous record of behavior and events. Additionally, this data compiles and makes up each machine’s history, making it easier for people and machine learning algorithms to spot trends and anomalies over time. 
2. Wireless connectivity carries data to a gateway. A wireless gateway collects readings from nearby sensors without relying on wired cables between machines. These readings are sent to the next stage of processing, acting as a bridge between the plant floor and central operations.
3. Edge computing processes data close to the machine. Processing machine readings where they’re generated has two benefits: it helps surface vital events sooner and reduces how much raw data is sent to the cloud. Sensors like Augury’s Halo™ R4000 series include edge-AI capabilities that adjust sampling to each machine’s operating conditions, which extends the sensor’s coverage to include low-RPM, intermittent, and variable-speed machines.
4. Cloud computing stores and scales data across sites. Pooling data into the cloud makes it easy to compare asset performance from multiple plants. The cloud also enables you to keep each asset’s full sensor reading history for any future analyses.
5. AI and machine learning algorithms compare live data to normal conditions, flagging deviations. AI and machine learning turn raw signal data into specific fault findings, such as bearing wear or misalignment, that teams can act on. Whenever a deviation is found, AI can point maintenance teams to developing issues before they snowball into unplanned downtime. 
6. Teams get alerts and can create (or approve AI-generated) work orders. This is IoT maintenance management at work. Teams get immediate intel into which machine is performing outside of normal conditions as well as insight into what’s most likely happening. By integrating with your CMMS and EAM platforms, these alerts can trigger work orders automatically.  

All these steps combined together make up IoT maintenance management: the use of live condition data to help teams plan, prioritize, and schedule work in a maintenance system. It’s also the methodology that moves teams away from fixed maintenance calendars and toward actual condition-based maintenance, ensuring teams spend time where it matters most.

Using IoT predictive maintenance to change daily work for your teams

IoT predictive maintenance moves your team from a state of “What’s due on the calendar?” to knowing what needs to be fixed, and how, in the correctly prioritized order. Live condition data, read against each asset’s own normal instead of fixed thresholds that treat every machine alike, turns that question into one of three clear decisions: schedule the repair, inspect the asset, or keep monitoring. Each decision gives you more lead time to plan work around production, with fewer surprises and less time spent on healthy machines.

Want to see how IoT predictive maintenance works on your equipment? Request a demo with our team. Request a demo with our team.

Frequently asked questions about IoT in predictive maintenance

• What should I look for in an IoT predictive maintenance solution?

  Make sure an IoT vendor’s solutions work for your equipment types and speeds, give teams actionable next steps instead of just alerts, and automatically create work orders in your computerized maintenance management system (CMMS) or enterprise asset management (EAM) platform. Compare sensor battery life, radio range, gateway capacity, and connection security, and confirm you can grow from a pilot to more assets and sites on the same platform.

• Where should you start with IoT predictive maintenance?

  Start with the assets where unplanned downtime costs you most. Match sensors to the fault types those assets are likely to develop, then choose a solution that connects to your current systems. Teams that begin this way build confidence in the data first, then expand to more assets and sites, which gives them more lead time to plan repairs on their schedule.

• What types of IIoT solutions support predictive maintenance?

  The most common IIoT solutions for predictive maintenance fall into three groups: condition monitoring platforms for collecting sensor data and flagging developing faults; maintenance management systems for planning, tracking, and scheduling work; and asset performance management software that connects the two. Choose the solutions that best match the equipment in your plant(s), how data moves between systems, and how readily your maintenance team can act on alerts.

• How do wireless sensors send data for predictive maintenance?

  Wireless sensors send readings a variety of ways, including over short-range radio or Bluetooth Low Energy, to a nearby gateway. The gateway forwards data to cloud analytics over Wi-Fi, Ethernet, or cellular. Since they don’t rely on cabling to each machine, battery-powered sensors make it possible to cover hard-to-reach assets. Be sure to ask an IoT vendor about each sensor’s battery life, radio range, how many sensors each gateway supports, and how the connection is secured.

• Which preventive maintenance systems integrate with IoT sensors?

  Both computerized maintenance management systems (CMMS) and enterprise asset management (EAM) platforms commonly integrate with IoT sensors through APIs, standard industrial protocols, or data gateways. Make sure any IoT vendors you’re looking at include sensor types and data formats that match the systems you use. That way, you should be able to have the sensors automatically trigger work orders and sync asset records between platforms, reducing manual maintenance workflow steps for teams and, ideally, address issues quicker.