Engine Failure Detection using IoT and ML monitors engine parameters like temperature, vibration, pressure, and RPM using ESP32 sensors. Machine learning models analyze real-time data to predict failures early, reducing breakdowns, downtime, and maintenance costs.

Machine learning–based predictive maintenance system for induction motors, using a Simulink digital twin and real ESP32 accelerometer data to detect electrical and mechanical faults with 92–100% accuracy.

Smart Factory Simulator for real-time IoT monitoring and predictive maintenance. It simulates 5 virtual machines with health degradation and integrates physical ESP32 sensors (temp/vibration). The FastAPI backend uses WebSockets for live data to an interactive React dashboard, supporting machine control via REST API.

Deployed CNN models on ESP32 for predictive maintenance (C++, TensorFlow Lite, ESP32)

AIoT predictive maintenance system using ESP32, MQTT, and LSTM to forecast machine failures in advance, enabling real-time monitoring, early warnings, and smart industrial automation.

Real-time IIoT monitoring system for CNC machines using ESP32, current and temperature sensors, and MQTT protocol. Enables predictive maintenance, remote alerts, and dashboard integration.

An IoT-based motor vibration monitoring system using ESP32 and MPU6050. Real-time data is sent to Firebase and visualized with a Streamlit dashboard for early fault detection and predictive maintenance.

Developed an ESP32-based predictive maintenance system with multi-sensor integration (temperature, vibration, sound, current, humidity) for real-time equipment health monitoring and fault detection using ML, enabling automated alerts via cloud dashboards.

Domestic vibration sensor with ESP32: detects and monitors motion and vibrations in real-time using a 6-axis MPU-6050 IMU. Battery-powered and Wi-Fi/Bluetooth enabled, ideal for smart home, IoT, and predictive maintenance applications.

Developed an IoT-based system for real-time monitoring and predictive maintenance of industrial motors using ESP32. Integrated sensors like ADXL345 (vibration), DHT22 (temperature & humidity), LM35 (temperature), and E18-D80NK (proximity) to detect anomalies. Data was vis

Edge-AI powered three-phase induction motor monitoring system featuring real-time electrical/vibration sensing, TinyML fault detection, predictive maintenance (EMA trend analysis), TFT UI, and a WiFi web dashboard. Built on ESP32-S3 with full open-source firmware and model pipeline.

Built an IoT-based predictive maintenance system using STM32 and a table fan motor. Collected current, vibration, and temperature data using multiple sensors. Applied unsupervised ML clustering to detect normal and anomalous conditions. Used ESP32 with AWS cloud for data storage, monitoring, and alerts.

IoT Predictive Maintenance system using ESP32, ADXL345, and DS18B20 sensors. Streams vibration & temperature data to a Flask server and Streamlit dashboard. Visualizes real-time data, triggers alerts on anomalies, and supports simulation. Full MIT license, step-by-step workflow, and Windows-ready firmware included.

(Final year Project) Designed an IoT-based Battery Monitoring System for EVs using ESP32 to stream real-time sensor data (temperature, humidity, voltage, current) to Firebase. Integrated a trained ML model via Google Colab for anomaly detection and alert generation (threshold-based and critical red alerts) and also for predictive maintenance.

No description available

ESP32-based predictive maintenance system using multi-sensor data (temperature, vibration via MPU6050, load via potentiometer, and LDR). Implements rule-based analysis to detect abnormal conditions and predict potential machine failures in real time.

Système IoT de maintenance prédictive basé sur ESP32 et MPU6050. Surveillance vibratoire en temps réel, filtrage de signal et alertes cloud via Blynk & MQTT

Some code for predictive maintenance on ESP32 e.g. vibration detection

Predictive Maintenance using ESP32 & ML

An ESP32-based predictive maintenance system for DC motors. Features real-time vibration analysis, non-contact thermal monitoring, current sensing, and RPM tracking with automatic safety cutoff via MOSFET. Designed for Industry 4.0 applications.

Predictive maintenance system with ESP32 and TinyML

No description available

Real-Time IoT Motor Health Monitoring with FreeRTOS, MQTT, and Blynk Dashboard. Production-ready predictive maintenance system for electric motors using ESP32 with concurrent real-time sensing, MQTT integration, and cloud alerts.

This project focuses on developing a high-speed data sampling system for predictive maintenance of hard-to-detect failures in industrial equipment, specifically targeting bearing failures at a sampling rate of 96kHz.

AI-Powered Predictive Maintenance System for Industrial IoT (ESP32/TinyML)

No description available

A predictive maintenance system built with an ESP32, FreeRTOS, and TensorFlow Lite for Microcontrollers.

ESP32-based predictive maintenance system using temperature and vibration analysis with MQTT and alerting

AI-driven predictive maintenance system for rotating machinery using Edge Impulse and ESP32. Hackathon 2025 submission.

A production-grade embedded firmware for real-time machine monitoring and predictive maintenance using ESP32/STM32 microcontrollers.