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The problem
Motors rarely fail without warning — bearings wear, misalignment grows, and vibration rises for days or weeks first. Industry watches this with condition-monitoring systems that cost more than the motors in a college lab, so students learn "vibration analysis" as a chapter and never touch a sensor. This project brings it to the bench: a small DC motor on a test stand, a MEMS accelerometer glued to its housing, a temperature probe on the bearing, and an ESP32 that streams RMS vibration, temperature and RPM to a dashboard over Wi-Fi. The dashboard plots trends, computes a live FFT spectrum, and logs threshold crossings. The student learns what 1x and 2x spectral peaks mean, sets alert bands, and watches a real wear trend develop — the full sense-to-dashboard pipeline of industrial condition monitoring, at student scale.
Small DC motor on the test stand whose health is monitored.
Follow the working sequence
How it works
- The ADXL345 is rigidly mounted on the motor housing; the DS18B20 probe sits on the bearing housing.
- The ESP32 samples acceleration at a fixed rate, computes RMS vibration velocity, and reads temperature and RPM.
- Every few seconds the node publishes the readings over Wi-Fi (MQTT/HTTP) to the dashboard backend.
- The dashboard appends the data to 24-hour trend charts and runs an FFT on the latest vibration window for the spectrum view.
- Thresholds (set by the student against ISO 10816-style zones) are checked on each update; crossings are written to the event log with timestamps.
- The student introduces a known condition (e.g. slight shaft misalignment via a shim) and observes the 1x/2x spectral response.
- Over a multi-day run, the rising RMS trend demonstrates bearing-wear detection — the core value proposition of condition monitoring.
Tech stack:
- ESP32 development board (Wi-Fi)
- ADXL345 MEMS accelerometer
- DS18B20 temperature probe
- Small DC motor on test stand
- Tachometer/pulse RPM input
- MQTT broker + web dashboard (HTML/JS)
- Arduino IDE (C/C++ firmware)
- 5 V power supply
| Parameter | Value |
|---|---|
| Vibration sensor | ADXL345 MEMS accelerometer, housing-mounted |
| Computed metric | RMS vibration velocity (mm/s), firmware-computed |
| Temperature | DS18B20 on bearing housing, alert limit configurable (design 80 C) |
| Speed input | Tachometer pulse, RPM displayed |
| Update rate | Every few seconds over Wi-Fi (design) |
| Spectrum | Live FFT with 1x shaft-frequency marking |
| Alerting | Configurable vibration/temperature thresholds + event log |
| Threshold basis | ISO 10816-style zones used as guidance; student sets final values |
Project features
- [Vibration sensing] ADXL345 MEMS accelerometer on the motor housing; firmware computes RMS velocity from the acceleration samples.
- [Bearing temperature] DS18B20 probe on the bearing housing tracks the thermal side of bearing health.
- [Live IoT dashboard] Web dashboard with 24-hour vibration and temperature trend charts, updating every few seconds over MQTT/Wi-Fi.
- [FFT spectrum view] Live spectrum with shaft-frequency (1x) marking — 1x/2x peaks indicate misalignment, the classic teaching signature.
- [Configurable alerts] Vibration and temperature thresholds with an event log; crossing a threshold raises a dashboard warning.
- [RPM input] Tachometer/pulse input records shaft speed so spectra are interpreted against the true running speed.
- [Wear-trend experiment] A documented procedure runs the motor over days and records the rising vibration trend — the student's own condition-monitoring dataset.
What is included
- Working motor health monitor (test-stand motor, sensor node, dashboard)
- Complete ESP32 firmware source code (sampling, RMS, MQTT publish)
- Dashboard web app (trends, FFT, event log, thresholds)
- Sensor mounting and wiring documentation
- Threshold-setting guide with ISO 10816 zone reference
- Wear-trend experiment procedure and data sheet template
- Project report PDF (condition monitoring theory, signal chain, methodology, experiment)
- PPT presentation for final review
- Viva Q&A preparation document (vibration analysis, FFT, RMS, ISO zones, MQTT)
- Setup and demonstration guide
Limitations & prerequisites
- A MEMS accelerometer on a small demo motor is a teaching proxy — it demonstrates the method, not certified industrial vibration analysis; no diagnostic claims beyond what the student's data shows.
- The FFT resolution is limited by the ESP32's sampling rate and window size; fine bearing-defect frequencies are indicative, not definitive.
- Thresholds are guidance-based (ISO 10816 zones); the student sets and justifies final values for their rig.
- Wi-Fi dropouts pause the dashboard feed; the node does not buffer long outages in the base build.
- Temperature and vibration trends need multi-day runs to show wear — a single-day demo shows the pipeline, not the trend.
Frequently Asked Questions
What does the FFT spectrum show?
It breaks the vibration signal into frequencies. A peak at 1x (shaft speed) with a smaller 2x peak is the classic misalignment signature — the student learns to read it, then creates it deliberately with a shim to verify.
What are the ISO 10816 zones?
An international standard's vibration-severity bands (A/B/C/D) for machines. They are used as guidance for setting the dashboard's alert thresholds; the student sets final values for their own rig.
Can it detect a bad bearing?
It demonstrates the detection method — rising RMS trend plus spectral changes. Definitive bearing-defect diagnosis needs higher-resolution analysis; this build teaches the pipeline honestly.
How does data reach the dashboard?
The ESP32 publishes readings over Wi-Fi via MQTT/HTTP every few seconds; the dashboard subscribes and updates the charts live.
What are the main limitations?
Teaching-grade MEMS sensing, limited FFT resolution, guidance-based thresholds, no long outage buffering, and wear trends need multi-day runs.
Is this project suitable for a final-year project?
Yes — for Mechatronics, Electrical and IoT programs. It demonstrates vibration analysis, embedded sensing, FFT, and IoT dashboards, all strong viva material. Suitable for B.E./B.Tech final-year projects in Mechatronics, Electrical and IoT engineering.
Components & software requirements
- ESP32 development board (Wi-Fi)
- ADXL345 MEMS accelerometer
- DS18B20 temperature probe
- Small DC motor on test stand
- Tachometer/pulse RPM input
- MQTT broker + web dashboard (HTML/JS)
- Arduino IDE (C/C++ firmware)
- 5 V power supply
Delivery information
Built-to-order project. Delivery timeline is shared after order confirmation based on current queue.
Support terms
Complete documentation, setup guide, and viva preparation included. Support for setup and explanation provided.
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