Edge AI for Industrial Equipment
Detect equipment anomalies on site, first.
XyloZero analyzes sound, vibration, temperature and other condition signals with PLC and drive data, detects anomaly events early, and feeds inspection and control-room decisions.

Press
Press / IR
Press coverage, talks, awards and company news.
ReadCase Study
STS crane rotating equipment condition monitoring
Anomalies in crane drivetrain signals that change with operating state and load, watched continuously.
ReadInsight
Field notes
Industrial safety, equipment diagnostics and field operating practices.
Read
What XyloZero Detects
What XyloZero detects
Equipment Condition
Changes in rotating and industrial equipment
Sound, vibration, temperature, magnetic field and other condition signals.
Operating Context
How the equipment is running
Running, stopped and load changes are taken into account to tell real anomaly events apart.
Early Warning
Events that lead to decisions
Selected events reach the control room, alerts and inspection priorities.
How It Works
Collect, context, select, act
Collect
Condition signals and PLC and drive data are collected on site.
Context
Operating state and site conditions are analyzed on the same timeline.
Select
Edge AI selects the anomaly events that matter.
Act
Events reach the control room, alerts, inspection priorities and follow-up.

- Top cover
- Sealing gasket
- MEMS microphones
- ESP32-S3 module
- Main board
- Battery
- Housing
- Mounting magnets
Industry Applications
Industries
XyloZero on equipment at ports, power plants and factories.
STS cranes, rotating equipment
Ports & logistics
Condition signals from crane machinery rooms, analyzed with PLC operating data.
Equipment
Motors, Bearings, Gearboxes, Brakes, Conveyors, Rotating equipment
Case study
STS crane rotating equipment condition monitoringTurbines, generators, coal handling
Power & plant
Condition signals from plant equipment whose loads change widely.
Equipment
Gas turbines, Generators, Conveyors, Pumps, Fans, Coal handling equipment
Case study
Continuous acoustic monitoring of coal handling equipmentProduction equipment, AGVs and AMRs
Manufacturing
Production equipment anomalies and AGV and AMR battery safety together.
Equipment
Motors, Pumps, Compressors, Fans, Production equipment, AGVs and AMRs
Case study
Acoustic validation of Li-ion battery fire precursors
XyloZero Battery Safety
Catch Li-ion batteryfire precursors first.
Before thermal runaway, a lithium-ion battery can change subtly in sound and ultrasound, not only in temperature. XyloZero analyzes these abnormal sound events to help raise an early fire warning.
Detect → Verify → Alert
- Sense battery signals
- Select abnormal events
- Alert the control room
- Respond on site
Where it applies
- AGVs
- AMRs
- Electrified prime movers
- ESS
- Battery packs

- XyloZero sensor node
- Main control box
Field-Proven Case Studies
Case studies

Logistics & Port
STS crane rotating equipment condition monitoring
- Problem
- Anomalies in crane drivetrain signals that change with operating state and load, watched continuously.
- Deployment
- Sound, vibration and temperature sensor nodes installed around the drivetrain
- Result
- Continuous drivetrain condition and anomaly monitoring

Power & Plant
Continuous acoustic monitoring of coal handling equipment
- Problem
- Sound from a fuel conveyor drive, collected without the gaps that walk-down inspections leave.
- Deployment
- Sensor node mounted on the upper belt conveyor drive
- Result
- Continuous collection without external power through the pilot

Manufacturing & Batteries
Acoustic validation of Li-ion battery fire precursors
- Problem
- A microphone array captured sound changes that appear before gas release is detected from a battery.
- Deployment
- Multichannel microphone array placed around the test battery
- Result
- Broadband sound rise caught before confirmed gas release in every analyzable test

Shipbuilding & Manufacturing
Shipyard welding equipment and crane condition monitoring
- Problem
- Operating state and operating types of welding equipment and a crane, estimated from attached sensor nodes alone, with no operating-signal link.
- Deployment
- Several sensor nodes per machine on welding gear drives and in the crane machinery room
- Result
- Operating state and work schedule estimated per machine without operating signals
AI for Operators
AI that supports operators, not replaces them
XyloZero is not meant to replace field workers. It helps them make safer, data-based inspection, maintenance and operating decisions.
- Less repetitive monitoring
- Inspection priorities built on anomaly events
- Fewer trips into hazardous areas
- Field experience combined with data
From PoC to Operations
From PoC to operations
Scope
Define the site, equipment, available data and operating problem.
PoC and validation
Collect data and validate anomaly events.
Deployment
Install XyloZero and connect PLC and control systems.
Operation
Device management, AI model updates and maintenance.
Expansion
From one machine to the same equipment class, the whole site and other sites.
Latest Insights
Latest insights
Field notes
Blog
Technical notes
Technical notes
News & IR
Press / IR
Answers
FAQ
