Predictive Maintenance avoids the drawbacks of Preventive Maintenance (under utilization of a part's life) and Reactive Maintenance (unscheduled downtime). Based on the health of an equipment in the past, future point of failure can be predicted in Predictive Maintenance. Thus, replacement of parts can be scheduled just before the actual failure.
AI-powered predictive maintenance & vibration fault diagnosis — bridging STEVAL-STWINBX1 IoT edge sensors and Claude via Model Context Protocol (MCP)
SMWiredPy is python library for Sensemore vibration/accelerometer sensors
Scalable framework for Predictive Maintenance applications
End-to-End Industrial AI-driven condition monitoring system for early fault detection in rotating machinery using Unsupervised LSTM Autoencoders. Detects failures 72h in advance. Early fault detection with 100% accuracy on high-frequency sensor data (NASA IMS Dataset).
Predictive maintenance modeling for wind turbines using sensor data. Forecasts potential failures in generator components to reduce downtime and maintenance costs.
Two-layer framework for artificial intuition: a fast anomaly scorer + selective LLM verification. Validated on NASA bearing run-to-failure data — verification helps when signal is ambiguous, hurts when it's already sharp. Grounded in Recognition-Primed Decision theory.
An IoT-based Machine Maintenance Monitoring System for Smart Factories. Utilizes ESP32, vibration, and temperature sensors for real-time condition monitoring and anomaly detection.
**Predictive Maintenance IoT** is an IoT-based system that uses sensors to continuously monitor the condition of machines and equipment. By collecting and analyzing real-time data such as temperature, vibration, and pressure, it predicts potential failures before they occur, helping reduce downtime, lower maintenance costs, and improve operational
Edge AI-powered industrial inspection platform for predictive maintenance using YOLOv8, Computer Vision, and Industry 4.0 technologies.
A research framework for benchmarking risk-aware time-series models in aerospace PHM. It focuses on de-noising complex flight manifolds, evaluating model stability under multi-modal regimes, and ensuring prognostic generalisation through rigorous experimental auditing.
Sovereign, climate-aware anomaly detection for critical infrastructure. One file. Zero dependencies. Zero data leaves your machine.
TensorFlow implementation of GSX (Gumbel-Sigmoid eXplanator) for instance-wise feature selection and explainable fault prediction from IoT vibration time series (IEEE Access 2022).
The back-end and front-end service for PTAI project.
Machine Learning pipeline for predictive maintenance in CNC machinery. Evaluates real-time IIoT telemetry using a Random Forest classifier to anticipate critical failures and optimize industrial downtime.
Industrial AI Predictive Maintenance Platform using Digital Twins, MQTT, TensorFlow, FastAPI, Docker, MySQL, and Flutter.
TinyML-based IoT system for electric motor fault detection using Edge Impulse and TensorFlow Lite Micro.
PMIS is a front-end web application built to serve as a comprehensive dashboard for monitoring industrial assets, predictive maintenance models, and operational workflows.
Add a description, image, and links to the predictive-maintenance-iot topic page so that developers can more easily learn about it.
To associate your repository with the predictive-maintenance-iot topic, visit your repo's landing page and select "manage topics."