Key Points
- AI condition monitoring software learns each asset's normal signature and flags named faults early, while threshold systems alarm only after a fixed limit is crossed.
- Retrofit fit, licensing model, and support terms shape a deployment's cost and success as much as the AI itself, so ask about them before the demo.
- Licensing can be per sensor, per asset, or subscription based; the illustrative 40-asset example below shows how the same plant can price very differently under each.
AI condition monitoring software takes continuous readings from sensors on motors, pumps, fans, and compressors and uses machine learning to spot the early signs of a fault. A threshold system waits for one value to cross a limit; AI software watches how vibration, temperature, ultrasound, and current change together against each machine's own baseline.
That difference matters most on the assets where equipment failure stops production: a boiler feed pump, an induced draft fan, a refrigeration compressor. The questions below help you compare platforms, kits, and licensing terms before you sign.
AI vs Threshold-Based Condition Monitoring
AI condition monitoring detects developing faults by comparing patterns across several signals with a learned baseline, while threshold monitoring alarms when one reading crosses a preset limit. In practice, AI tends to warn earlier and name the fault; thresholds are simpler but fire later and leave diagnosis to your team.
| Threshold-based monitoring | AI condition monitoring | |
|---|---|---|
| How it decides | One value against a fixed limit | Several features against a learned baseline and known fault patterns |
| What the alert says | "Vibration high" or "temperature high" | Named fault (for example, outer race defect), severity, evidence |
| Operating changes | Speed or load changes can cause false alarms | Baselines per operating state reduce false alarms |
| Setup | Engineer sets limits point by point | Learning period, then review of early alerts |
| Who interprets | Vibration analyst or experienced technician | Software proposes the diagnosis; team confirms |
| Best fit | Simple, constant-speed, low-criticality assets | Critical and variable-speed assets |
Failure-mode walkthrough: outer race defect on an ID fan motor (illustrative)
Illustrative scenario. It shows how each approach behaves; it is not data from a specific plant or product.
A VFD-driven induced draft fan runs between 45 and 60 Hz. A small spall forms on the outer race of the motor's drive-end bearing.
- Early stage. The defect produces impacts at the bearing's ball pass frequency, outer race (BPFO). They show up in the envelope spectrum and as rising kurtosis. Overall vibration velocity barely moves, so a threshold on overall velocity stays silent. An AI model tracking envelope features against the fan's baseline at each speed flags an early-stage outer race defect.
- Developing fault. Ultrasound picks up increased friction and impacting as lubrication degrades around the damage. The AI raises severity and recommends inspection and lubrication at the next planned stop.
- Late stage. Overall velocity and bearing temperature rise. Now the threshold alarm fires, but the bearing is close to failure, and the repair may need to happen at a time the production schedule did not choose.
The same defect, the same sensors: the difference is which features the software watches and whether it accounts for speed changes. This is the core of industrial vibration analysis, automated.
Retrofit and Legacy Equipment: What to Ask
Most AI condition monitoring deployments are retrofits: sensors added to machines that were never designed for them. Before buying, confirm what the kit contains, how hard it is to install on your oldest assets, and whether it works with sensors you already own.
Kit options
AI condition monitoring kits usually include wireless sensors, mounting hardware, and a way to move data off the floor, either a gateway or built-in cellular. Electrical monitoring adds current transformers or clamp-on sensors installed in the motor control center.
Ask the vendor to list exactly what ships per asset type. A centrifugal pump train often needs sensing points on both the motor and the pump, while a small exhaust fan may need one.
Installation complexity
Mounting method affects data quality. A stud mount transmits high frequencies best, an epoxy pad is close behind, and a magnet is fastest but can lose the high-frequency content that early bearing faults produce.
Questions to ask:
- Can sensors be installed while the machine runs, or is a lockout needed?
- Does electrical monitoring require an electrician in the MCC?
- Who installs: your team, the vendor, or a contractor?
- How long does a typical asset take, and what does the vendor need from you beforehand?
Sensor compatibility and legacy data
Older machines often lack nameplate details. Without bearing part numbers and shaft speeds, the software cannot calculate fault frequencies precisely, so ask how the vendor handles unknown components.
Also check environment ratings (temperature range, washdown, hazardous area certifications) and whether the platform can ingest existing industrial IoT sensors, historian tags, or PLC data alongside its own hardware.
Licensing Models Explained
AI condition monitoring software licensing generally follows one of three models: per sensor, per asset, or a SaaS subscription by site or tier, sometimes with hardware sold or leased separately. Each model shifts cost and risk differently, so compare them on the same asset list.
| Model | How it is priced | Works well when | Watch for |
|---|---|---|---|
| Per sensor | A fee per installed sensor or measurement point | Simple assets with one or two points each | Pressure to under-instrument complex machines |
| Per asset | A fee per monitored machine, regardless of sensor count | Mixed fleets with multi-point machines | How the vendor defines an "asset" (motor and pump as one or two) |
| SaaS subscription | Site, tier, or platform fee, often with user or feature limits | Large rollouts with predictable scope | Seat limits, feature tiers, and data retention caps |
| Hybrid | Hardware bought or leased, software billed separately | Teams with capital budget for hardware | Hardware refresh cycles and battery replacement costs |
Illustrative example: one plant, two licensing models
Illustrative only.
A plant wants to monitor 40 assets:
- 25 motor-pump trains, with three sensing points each (motor drive end, pump drive end, pump non-drive end) = 75 points
- 10 fans, with two points each = 20 points
- 5 air compressors, with four points each = 20 points
That is 40 assets but 115 sensing points. Under per-sensor licensing, the bill scales with 115 units, and someone has to decide whether each pump really needs its third sensor. Under per-asset licensing, the bill scales with 40 units, and the decision about sensor placement becomes an engineering question instead of a budget one.
Neither is wrong. The point is to price both models against the same list so the comparison is honest. For budgeting and ROI math, see AI condition monitoring cost.
What to watch for in any contract
- Who pays for replacement sensors and batteries
- Minimum terms, auto-renewal, and annual price escalators
- Whether analyst review or expert support is included or billed separately
- Data ownership and export rights if you leave
- Charges for adding assets mid-contract
6 Criteria for Evaluating a Platform
The six criteria to evaluate an AI condition monitoring platform on are multimodal coverage, edge deployment, CMMS integration, alert explainability, SLA terms, and support model. The first four decide what the software can detect and how alerts reach your team; the last two decide what happens when something goes wrong.
1. Multimodal coverage
Multimodal monitoring combines several signal types so the software can catch faults that one signal misses, which is also what Tractian's sensors are built around: ultrasound for early lubrication problems, vibration for misalignment, temperature for friction. Ask which signals each sensor measures natively and which require add-on hardware.
2. Edge deployment
Find out where processing happens and how data leaves the asset. Platforms that process locally or transmit over cellular can avoid plant network approvals, which shortens IT review. Our guide to edge AI for industrial plants explains the tradeoffs.
3. CMMS integration
Alerts should land where your planners already work, so detection feeds your predictive maintenance program instead of a separate screen. Ask for prebuilt connectors to your CMMS or ERP, an open API, and a live demonstration of an alert becoming a work order with evidence attached.
4. Alert explainability
Your technicians need to know why the software made a call. Look for alerts that name the fault, show the spectrum or trend behind it, state severity, and suggest the next step.
5. SLA terms
Ask what the vendor commits to in writing: platform uptime, support response times, and how missed detections or false alarms are reviewed. Accuracy commitments are rare and hard to define, so read the measurement terms closely.
6. Support model
Ask who installs, who trains your team, who you call when an alert is unclear, and whether a named contact is assigned to your site. Support matters most in the first months, when technicians are learning to trust the alerts. Our guide on how to choose an AI condition monitoring provider turns these criteria into a provider scorecard.
Top Platforms
The platforms below are frequently compared by industrial buyers looking for AI condition monitoring. Competitor details summarize each vendor's own website; confirm current capabilities with the vendor.
Tractian
Tractian is a leader in physical AI, a branch of industrial AI, focused on asset-heavy operations. Its platform brings together condition monitoring, electrical monitoring, and maintenance tools for equipment ranging from motors, pumps, and compressors to chillers, cooling towers, presses, and HVAC systems.
Sensing. The Smart Trac Ultra sensor measures vibration, ultrasound, temperature, and magnetic field in one battery-powered device, sampling from 0 Hz to 64,000 Hz. It is rated IP69K and certified for Class 1 Div 1 hazardous locations. Tractian's multimodal coverage adds electrical (power, current, voltage) and operational signals, so the team sees the mechanical and electrical health of an asset in one view.
Connectivity. The sensor captures data on the asset, a gateway relays it over 4G/LTE, and the AI analysis runs in Tractian's cloud. Plant Wi-Fi is not required, which suits retrofits on legacy lines with restricted networks.
Diagnostics. The AI maps signal patterns to failure modes in its detection library, such as bearing wear, misalignment, unbalance, cavitation, and lubrication failures. Alerts are ranked by severity and asset criticality and state the root cause and recommended next steps.
Integration. Tractian includes native work order and preventive maintenance tools and connects to existing CMMS, ERP, SCADA, and APM systems through APIs, with SAP, IBM Maximo, and Oracle NetSuite among its listed integrations.
Licensing and support. Pricing is asset-based and scoped to criticality, rather than a flat sensor count. Certified specialists handle installation and training, and Tractian assigns a dedicated technical account manager after setup. Tractian's standard deployment gets customers fully operational within 14 days.
Customers. Published case studies include Pirelli (98% alert check-in rate and 77 failures identified), Danone (3 to 30 days of production stoppage avoided), and Lyka (two critical equipment issues detected in the first week).
Augury
Best for: plants that want AI alerts on critical rotating equipment validated by analysts, backed by expert support.
Notable features: per Augury's website, its Machine Health sensors capture triaxial vibration, temperature, and magnetic flux data, and the company also lists ultrasound among its sensor signals. Data is processed in the cloud, where AI diagnostics are paired with expert validation, and diagnostics include fault severity, root cause, and recommended actions. Its Halo Hub connects over Wi-Fi or LTE cellular.
Questions to Ask Before You Buy:
- How long does analyst validation add before an alert reaches my team?
- Can my team view the spectra behind each call?
- Can sensors already installed on my secondary assets feed the platform, or does each monitored asset need the vendor's hardware? Several G2 reviewers mention they could not connect existing third-party sensors (1, 2, 3).
- How long from contract to first validated alert at a plant like mine, and how much of that work falls on my team? Several G2 reviewers describe sensor deployment and system integration as a larger upfront effort than expected (1, 2, 3).
- How is coverage priced when I extend from critical machines to secondary pumps, fans, and conveyors?
- How do alerts become work orders in my CMMS, and who maps the fields? Some G2 reviewers note that the handoff depends on fast triage and, with some CMMS setups, custom field mapping (1, 2).
- How flexible are cross-site reports and exports? Several G2 reviewers mention wanting more customizable report filters and direct exports (1, 2, 3).
- Which sensor models carry certifications for my classified hazardous zones?
Reviewers also credit the platform with early warnings that leave time to schedule work while the machine is still running (G2) and with alerts ranked so teams know what to handle first (G2).
KCF Technologies
Best for: plants that want several sensor types, including existing piezo sensors, in one platform.
Notable features: per KCF's website, its platform takes in vibration, ultrasonic, oil humidity, motor current signature (MCSA), pressure, and legacy IEPE sensor data, some of it through third-party sensors on its IoT HUB. Sensors send data across a mesh of Base Stations along the strongest signal path, and Base Stations connect through plant Ethernet or a built-in cellular modem. A Piezo Sensing option covers slow-speed and high-speed assets, and DeskAI fault detection runs in its SMARTdiagnostics software.
Questions to Ask Before You Buy:
- How many Base Stations and repeaters does my floor plan need?
- Which signals come from KCF sensors and which need third-party devices?
- Who reviews alerts before they reach my team?
AssetWatch
Best for: plants that want monitoring as a managed service, with hardware, software, and a named condition monitoring engineer in one subscription.
Notable features: per AssetWatch's website, vibration, temperature, electrical, and oil analysis data feed one AI-driven platform, and a certified Condition Monitoring Engineer (CME) reviews findings before they reach the plant team. Sensors connect through cellular hubs included in the installation, so no plant network integration is required. An in-panel electrical sensor tracks three-phase current, runtime, and panel temperature on AC motors. Billing is one subscription covering hardware, software, and service, with no capital expense, and a $199 trial is offered.
Questions to Ask Before You Buy:
- How long does CME review add before an alert reaches my team?
- Can my analysts view the spectra behind each finding?
- Which signals come from on-machine sensors, which from the panel sensor, and which from lab oil samples?
- Will the sensor fit my mounting points and operating temperatures? Some G2 reviewers mention sensor size or temperature limits at specific locations (1, 2).
- How long from contract to all sensors online at a plant like mine, and how much of that work falls on my team? Some G2 reviewers describe setup and commissioning as the most demanding phase (1, 2).
- How are cost savings reported against subscription spend? Some G2 reviewers mention wanting more detail on cost savings (1, 2).
- What happens to the hardware and asset history if we end the subscription?
Reviewers also credit the platform with spotting failures early enough to schedule repairs (G2) and with professional, responsive technical support (G2).
Waites
Best for: teams that want wireless vibration and temperature monitoring with analyst support, plus a path for existing accelerometers and analog instruments.
Notable features: per Waites' website, its wireless mesh uses 2.4 GHz IEEE 802.15.4 radios with a line-of-sight range of roughly 3,000 ft, and gateways send data to the cloud over 4G/LTE. ImpactVUE high-frequency processing is built into several of its sensors. A PiezoNode connects legacy or Waites high-temperature accelerometers, and a Universal Adapter brings in third-party analog sensors such as RTDs and 4-20 mA devices. Waites states that CAT II+ certified vibration analysts review AI alerts before they reach the plant team.
Questions to Ask Before You Buy:
- How long does analyst verification take on a typical alert?
- Which signals are native to the node and which need an adapter?
- How are alarm levels set, and by whom?
- What battery life does a node reach at my sampling rate, and who pays for replacement batteries and nodes under the contract? One technician wrote on Reddit: "Those batteries are most likely not going bad that fast, but just not making contact due to the warping waites nodes." (u/Critical-Practice-92, December 2024).
- What training do technicians get on reading velocity and acceleration trends before alerts go live? One maintenance technician wrote on Reddit: "The velocity part is my biggest issue" (u/Reasonable-Purple112, December 2024).
Users also report early catches: a maintenance planner at a cement plant wrote on Reddit that the monitors "probably saved us thousands and kept us from having several catastrophic failures" (u/3kimully, November 2024).
For a wider view that includes predictive maintenance platforms, see our ranking of AI predictive maintenance software.
Frequently Asked Questions
What is AI condition monitoring software?
AI condition monitoring software collects data from sensors on equipment such as motors, pumps, and fans and uses machine learning to detect developing faults. It flags named problems like bearing wear or misalignment, with a severity level, before they lead to breakdowns. It is the software layer of asset condition monitoring, and our guide to AI condition monitoring explains how it works.
Who offers scalable AI condition monitoring solutions?
Several vendors sell AI condition monitoring built to grow from a pilot to multiple sites, including Tractian and the other platforms listed above. To judge whether a solution holds up at scale, check whether pricing grows predictably per asset, whether installation needs IT work at each site, and whether one dashboard covers multiple plants. Tractian's gateways relay sensor data over 4G/LTE, which avoids a new network project at each site.
Who offers turnkey AI condition monitoring packages?
A turnkey package covers sensors, connectivity, software, installation, and training under one contract. Tractian's offering includes sensors, gateways that relay data over 4G/LTE, the AI platform, installation and training by certified specialists, and native work order tools. Ask any vendor for a written list of what is included and what is billed separately.
Who supplies retrofit AI condition monitoring kits?
Many sensor-based AI condition monitoring vendors supply retrofit kits for existing equipment, including Tractian. A typical kit includes wireless sensors, mounting hardware, and a connectivity path such as a gateway or built-in cellular. Confirm mounting options, environment ratings, and how the vendor handles older machines with missing bearing or nameplate data.
Which AI condition monitoring providers offer SLA-backed guarantees?
SLA terms vary widely, so request each provider's SLA document rather than relying on sales materials. Common commitments cover platform uptime and support response times; written accuracy commitments are less common and depend on how missed detections and false alarms are measured. Read the definitions and remedies before comparing providers.
How is AI condition monitoring software licensed?
AI condition monitoring software is usually licensed per sensor, per asset, or as a subscription by site or tier, with hardware sometimes sold or leased separately. Per-asset pricing is easier to compare across machines with different sensor counts. Tractian uses asset-based pricing scoped to which equipment is critical.
What is included in an AI condition monitoring kit?
Kits commonly include sensors, mounting hardware (studs, epoxy pads, or magnets), and either a gateway or built-in cellular connectivity. Electrical monitoring kits add current or voltage sensors for the motor control center. The software license, installation, and training may or may not be part of the kit price.
What are the best industrial AI platforms for condition monitoring?
The right platform depends on your assets, network rules, and maintenance workflow. Shortlist platforms that measure the signals your critical failure modes produce, integrate with your CMMS, explain their alerts, and offer clear SLA and support terms. Pilot two of them on assets with known problems before a full rollout.
Can AI condition monitoring work on old equipment?
Yes, in most cases. Wireless AIoT sensors mount externally on bearing housings and motor frames, so machines decades old can be monitored without modification. The main limit is missing component data, which makes precise fault frequency calculations harder until bearing models and speeds are confirmed.
See how Tractian detects failures before they happen. Explore Condition Monitoring