Meet the new standard for industrial machines

Machines often fail without warning. A worn bearing or a loose part stops the line, and repairs cost more when they are not planned. Waiting for a breakdown is no longer enough.

INOWISE.IO turns your machines into connected assets. Sensors learn what normal looks like. When something changes, you get an alert early.

How the Ecosystem Works

The Foundation (Sensors): Small sensors measure vibration, sound, temperature, pressure, force, voltage, motor condition and more. AI cameras check products on the line.

The Brain (Edge AI and Network): A vibration sensor can produce 50,000 values per second. Edge AI analyses them on the sensor and sends only the result. Alerts arrive in milliseconds, even without a permanent internet connection. Data travels over LoRaWAN or NB-IoT to the INOWISE.IO platform.

The Outcome (Digital Twin and Rules): The Digital Twin puts every signal on a live map of your site. Click on a machine and see what is wrong, where it is, how urgent it is and what to do. A simple rule turns it into action: if the bearing score is above 0.85, a work order goes to maintenance.

Connect your sensors

• Vibration and acceleration sensor
• Acoustic emission and sound sensor
• Temperature and humidity sensor
• Pressure and force sensor
• Voltage and motor condition sensor
• Material thickness sensor
• CO2 and gas sensor
• AI camera (image recognition)
• GPS tracker

What Edge AI can sense

• It sees: An AI camera spots a surface defect at a station. No video is sent, only the finding.
• It hears: A change in a motor’s sound can point to a worn bearing.
• It feels: Vibration patterns show imbalance, misalignment and wear. A machine health score can drop from 94% to 63% long before the machine stops.
• It smells: Gas sensors in an electrical cabinet found an overheating cable 18 minutes before there was any smoke.

Predictive vibration analysis

Small sensors learn the normal vibration pattern of a machine. If an unusual movement appears, such as imbalance, loosened parts or early signs of wear, the system flags it at once. Because the analysis happens on the device, alerts are fast and reliable. This means fewer breakdowns, less downtime and lower maintenance costs.

From alert to action

One alert is helpful. Hundreds of alerts are noise. The Digital Twin shows the full picture. For example: Motor 17 has bearing wear, it sits on Line 2, it has about 14 days to failure, and it should be inspected this week.

Which transmission protocol fits?

• LoRaWAN: Long range, very low power, and you can run your own network. Good for single sites and machines in motion. Two-way communication. Battery life of 5 to 10 years.
• NB-IoT: Uses the cellular network, so no own gateway is needed. Excellent coverage indoors and in dense urban areas. Faster response times. Battery life of 3 to 7 years.
• Sigfox: Lowest cost radio modules. Best for simple devices that send very small amounts of data rarely.
• Cellular 4G and 5G: High speed for large amounts of data, but very high power use. Not ideal for battery sensors.

Edge AI meets LoRaWAN

Many machines, from factory motors to farm pumps, cannot be monitored easily. Cloud data is often too expensive, too slow or impossible where the connection is poor. Edge AI with LoRaWAN solves this. Devices run for years on very little power, detect problems locally and instantly, keep data private, work in remote or harsh places and scale to thousands of devices.

Why This Changes Everything

The real value of Industry 4.0 is clarity. You do not need more data. You need to understand the data you have. INOWISE.IO enables predictive maintenance with the infrastructure you already own: less downtime, lower costs and faster decisions.

Project Impact in Numbers