Key Points
- Not all multimodal sensing is equal. The label gets stretched, so judge a sensor on what it actually captures and how well, not on the word on the box.
- Prioritize real coverage from one device, signal quality that can isolate specific faults, and handling for your hardest assets: intermittent, variable-speed, and low-speed equipment.
- The sensor is only as good as the intelligence behind it. A data stream is not a diagnosis. Look for named faults, severity, and a recommended action, not raw threshold alarms.
- Check the practical things that quietly kill deployments: connectivity that does not lean on plant Wi-Fi, multi-year battery life, environmental ratings, and plug-and-play install.
- The best setups close the loop, turning a diagnosis into a scheduled work order in the same system.
"Multimodal" is not a guarantee
If you are reading this, you have most likely already decided that multimodal sensing beats a single-parameter sensor. Good. The harder question is which multimodal sensor to actually put on your machines. Get that choice right and you have a predictive maintenance sensor that earns its place for years. Get it wrong and you have an expensive data logger.
That question matters more than it looks, because "multimodal" has become a label people stretch. Some systems capture several signals from one purpose-built device. Others bolt a second sensor onto a vibration-only platform, or lean on third-party hardware to fill the gaps, and still call the result multimodal. On a spec sheet they can look alike. On your plant floor they are not.
Here is what actually separates a multimodal sensor worth deploying from one that will disappoint you six months in. The criteria below are ranked, so the ones that matter most come first.
1. Real multimodal coverage, from one device
The first question is simple: how many signals does the sensor capture, and does it capture them from a single point on the machine?
Single-point coverage is not a convenience feature. When vibration, ultrasound, temperature, and magnetic field all come from the same device at the same spot, those signals are genuinely correlated, which is the whole reason multimodal sensing works. Stitch them together from separate sensors mounted in different places and you lose some of that correlation, add install points, and multiply the things that can drift or fail.
Watch for "multimodal" that really means "we support other sensors you buy and wire up separately." That is a different product with a different total cost and a different failure surface. A true multimodal sensor delivers the coverage in one install, one power source, and one data stream.
2. Signal quality good enough to isolate faults
A sensor that tells you vibration is up has given you an alarm. A sensor that tells you which fault is developing has given you a decision. The gap between those two is signal quality.
Three specs decide it. Frequency range determines how many fault types the sensor can even see. Sampling rate and spectral resolution determine whether it can separate a bearing defect from misalignment from looseness, rather than lumping them into one raised reading. And triaxial vibration measurement captures motion in all three directions, which matters because different faults show up on different axes.
On the ultrasound side, look for high-frequency capture. Early friction and lubrication problems live in the ultrasonic range and show up there well before they reach the vibration spectrum. A sensor that only reports overall levels, without the resolution to isolate specific frequencies, will keep your team guessing.
3. Coverage for your hardest assets
Most sensors do fine on a motor that runs at a constant speed all day. Your hardest problems are rarely those machines. They are the ones that make monitoring difficult, and a predictive maintenance sensor is worth what it delivers on those.
Ask how it handles three cases. Intermittent machines, where the sensor has to capture data at the moment the asset is actually running instead of missing it between samples. Variable-speed and VFD-driven assets, where the system needs to track real-time RPM to make sense of the data, ideally without an external tachometer bolted on. And low-speed bearings, where vibration signatures are weak and ultrasound often carries the earliest warning. If a sensor only performs on easy assets, it will miss exactly the equipment that keeps you up at night.
4. Connectivity that does not depend on your IT team
This is where deployments stall more often than people expect. A sensor that relies on plant Wi-Fi inherits every dead zone, every network policy, and every IT queue in the building.
Look for connectivity that runs independent of the plant network, whether cellular or a dedicated sub-GHz link. It means you can put sensors in the far corners of the plant without chasing signal, and you can deploy without waiting on network access that may take weeks to arrange. The best hardware in the world does not help if it cannot reliably get its data out.
5. Built to survive the plant, and easy to install
The sensor lives on the machine, so it has to take what the machine takes. Check the environmental ratings against your reality: an IP rating high enough for washdowns and dust, ATEX or IECEx certification if you have hazardous areas, and an operating temperature range that covers your worst spot in summer and winter.
Then check the two numbers that govern total effort. Battery life should be measured in years, not months, or you have traded one maintenance route for another. And installation should be wireless and plug-and-play, so you can retrofit existing equipment in minutes rather than rewiring the plant. Fast install is not just about launch day. It is what lets you scale a predictive maintenance sensor from a pilot to hundreds of assets without a project plan for each one.
6. Intelligence, not just data. This is the one that matters most.
Here is the criterion that separates the field, and the one buyers most often underweight. A predictive maintenance sensor that streams raw data has handed your team a second job: interpreting it. That works only if you have vibration analysts on staff with time to spare, and most teams do not.
What you actually want is a system that fuses the modalities and returns a named fault, its severity, the evidence behind it, and a recommended action, prioritized by how critical the asset is. Threshold alerts produce alert fatigue, because everything eventually crosses a line and your team learns to ignore the noise. Diagnoses produce decisions, because they tell a technician what is wrong, how urgent it is, and what to do.
So push hard on this in any evaluation. Does the platform name the fault, or just flag an anomaly? Does it estimate severity and time to failure, or leave that to you? Does it require a specialist analyst to translate the data, or does the AI do that work? The sensor is the easy part. The intelligence reading it is what you are really buying.
7. A closed loop into maintenance execution
A diagnosis that lands in a standalone dashboard still has to be re-keyed into your CMMS before anyone can act on it. That handoff is where good detection quietly leaks value.
The strongest setups close the loop. The finding becomes a work order in the same platform, with the recommended procedure and the parts list attached, ready to schedule. Detection only pays off when it turns into a scheduled fix, and the fewer systems and manual steps between the alert and the technician, the more often that actually happens.
8. The data and R&D behind the AI
AI is only as good as what trained it, so look past the word and ask what is underneath. How much asset data has the model learned from? Does the vendor run genuine, ongoing research, or is "AI" a label on top of basic analytics that has not moved in years?
It shows up in results. A platform that already knows the expected fault frequencies for your specific motor and bearing models will catch problems faster and raise fewer false alarms than one starting from a blank slate on your equipment. Scale of data and real investment in the models are not marketing. They are the difference between a system that gets sharper over time and one that plateaus.
A quick checklist
Before you sign anything, make sure your multimodal sensor clears these:
- Multiple modalities captured from one device, one install point.
- Frequency range, sampling, and resolution high enough to name faults, not just flag them.
- Proven handling of intermittent, variable-speed, and low-speed assets.
- Connectivity independent of plant Wi-Fi.
- Ratings, battery life, and install that fit your environment and scale.
- AI that diagnoses, prioritizes, and prescribes, not just alerts.
- A closed loop from diagnosis to work order.
- A serious data foundation and active R&D behind the models.
How Tractian measures up
Run Tractian's Smart Trac sensor through that list and it holds up, which is exactly what it was built for.
It captures vibration, ultrasound, temperature, and magnetic field from a single point on the machine, so you get true multimodal coverage from one wireless device instead of a rack of separate instruments. The signal quality is there to name faults, with triaxial vibration measured up to 64,000 Hz and continuous ultrasound up to 200 kHz. For the hard assets, patented features handle the cases other systems miss: capturing data on intermittent machines, tracking real-time RPM on variable-speed equipment without an external tachometer, and correlating signals across sensors on the same machine.
On the practical side, it communicates independent of plant Wi-Fi, carries an IP69K rating with ATEX/IECEx certification for hazardous areas, and runs for years on a single battery, with a plug-and-play install measured in minutes. Then the part that matters most: Tractian's AI fuses those signals and returns a named diagnosis across dozens of failure modes, with severity and a recommended action attached, prioritized by asset criticality, and it flows straight into work orders and procedures in the same platform. All of it is backed by Tractian Labs and an asset library covering millions of motors and tens of thousands of bearing models, so the models already know what your equipment should look like.
If you are evaluating multimodal sensors right now, the fastest way to compare is to see one work on your own equipment. Book a walkthrough with our team, and we will show you what Smart Trac flags on your most critical assets.
