On-device Vibration Anomaly & Tool Wear

Edge AI · MI-EAI-02

An IMU microcontroller board on a spindle or tool holder classifying normal, worn and abnormal vibration on the device, sending only events over BLE or LoRa — a data lake is not required.

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• ₹7,000pilot BOM · hardware only
• 4 – 6 daysto a live pilot
• Intermediateintegration level
• 3validated providers

Why the plant needs a verdict, not a data lake

The problem

Sending raw vibration data to the cloud costs bandwidth and money and still needs someone to analyse it. The plant does not want a data lake; it wants a verdict at the machine: is this tool worn, is this spindle abnormal, should we stop.

• Raw vibration streaming is expensive and rarely analysed
• Tool wear is judged by part quality after the fact
• Cloud analytics need connectivity the shop floor may not have
• No per-machine verdict, just data

The solution

A microcontroller board with an accelerometer and gyro is mounted on the spindle housing or tool holder. A TinyML classifier trained on a few hours of normal, worn-tool and abnormal operation runs on the board and sends only classification events — normal, worn, abnormal — over BLE or LoRa to a gateway, with an optional relay for a machine stop.

• Normal / worn / abnormal classification on the device
• Events only over BLE or LoRa; no raw data streaming
• Optional relay output for a machine stop or light
• Tool-change-on-condition instead of by count

Validated service providers

Validated by TwoElectrons and ranked by validation score: verified credentials, completed jobs on the platform and client ratings.

1. Sample Provider S — Bengaluru · On-prem LLM & RAG · ★ 4.9 · 12 jobs
2. Sample Provider T — Hyderabad · TinyML & edge inference · ★ 4.7 · 9 jobs
3. Sample Provider U — Pune · Secure AI infrastructure · ★ 4.6 · 7 jobs

Provider listings are samples pending live registrations.

Pilot BOM and cost

Reference hardware to run the pilot. Unit costs are indicative Indian market prices for the component class.

POC BOM cost only — hardware for a pilot. Installation, integration and provider services are quoted separately. Request for Quote

How on-device vibration classification works

1. Mount. Fix the IMU board on the spindle housing or tool holder.
2. Capture. Record normal, worn-tool and abnormal windows with the operator.
3. Train. Train a classifier in Edge Impulse; deploy to the board.
4. Act. Send events to the gateway; trigger a tool-change light.

Pilot architecture

IMU MCU board (accel + gyro · magnetic mount) → On-device classifier (normal · worn · abnormal) → BLE / LoRa gateway (events only) → Tool-change light / dashboard (condition-based)

Classification happens at the sensor; the network carries only 'normal', 'worn' or 'abnormal' events.

Business case: tool life, scrap, bandwidth

Ranges are typical figures reported for this class of solution; your pilot establishes the numbers for your plant.

• Events, not data on the network (kilobytes a day instead of gigabytes)
• Condition-based tool changes (fewer scrapped parts, longer tool life)
• Seconds from abnormal to a stop signal (optional relay)
• 4 – 6 days to a live pilot (one machine)

Who this is for: Machining and tool-room engineers, Reliability teams with limited connectivity, CNC turning and milling, Grinding and stamping, Woodworking and composites, Plants that do not want a data lake.

Illustrative case study

Illustrative scenario · not a client reference

A CNC turning cell machining hardened shafts in Faridabad (illustrative)

Set-up. IMU board on the turret tool holder, two hours of recording per class with the operator marking tool state, classifier deployed, events over BLE to a cell gateway and a yellow light on the machine.

What happened. The 'worn' class fired on average 40 parts before the count-based tool-change point, and in two cases 'abnormal' preceded a chipped insert. Tool changes moved to condition-based; scrap on the shaft dropped by about a fifth in the following month.

• Pilot budget: ₹7,000 hardware
• Worn-tool warning: ~40 parts early
• Scrap reduction: ~20%

FAQ

How much training data is needed?

A few hours covering normal, worn and abnormal conditions is enough for a pilot; the operator marks tool state during recording.

Does it need connectivity to work?

No — classification runs on the board; the relay or light works with no network at all. BLE or LoRa is only for logging events.

Will it transfer to another machine?

The model is trained per machine type; similar machines can share a model with a short validation.

What does the ₹7,000 cover?

The IMU board, radio add-on, mount and enclosure on a free-tier training workflow. Training effort and provider services are quoted after a Request for Quote.