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Manufacturing / Indeema product: AI machinery condition monitoring

Condition Monitoring System Development With Vibration Diagnostics

Vibration sensing, signal processing and AI diagnostics for motors, gears and bearings.

Vibration diagnostic unit beside the iReDS monitoring dashboard.

The opportunity

Knowing which part is failing

Maintenance teams see that a machine vibrates abnormally, but need to know which part is failing and how it fails. The product had to collect, interpret and present the signals.

The solution

From sensor to diagnosis

iReDS combines accelerometer inputs, an acquisition unit, reference-level alarms and AI that identifies the type of fault, with views to review the data.

  1. 01Accelerometers
  2. 02Acquisition unit
  3. 03Signal processing
  4. 04AI diagnosis
  5. 05Dashboard
iReDS system diagram showing sensor inputs, diagnostic hardware, USB and Ethernet connections, cloud processing and user interfaces.

The impact

What changed for maintenance teams

  • Abnormal vibration only noticedAlarms against reference levels
  • Fault type unknownAI identifies the type of fault
  • Raw signalsDashboards and reports

AI identifies the type of fault; the maintenance team decides what to do.

Engineering judgement

Decisions that shaped the product

Specify measurement conditions first

Sensor and controller requirements were framed around the target frequency range, sensitivity, operating conditions and signal path.

Keep signal processing explicit

Embedded acquisition and Fourier-based signal processing prepare clean, comparable inputs, so the AI works on well-defined features rather than raw noise.

Classify the fault, not just the anomaly

Reference levels raise the alarm, and the AI names the type of fault, so the alert tells the maintenance team what to inspect.

Support local or cloud processing

iReDS offers local and cloud-based processing as deployment options rather than requiring one processing location.

Built with

C++ · Qt · Digital signal processing · Fourier transform · ADXL345 accelerometer · ATmega328P controller · Machine learning (fault classification)

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