Senior Design Project 2026

AI Industrial Equipment Maintenance & Energy Monitoring

Next-generation predictive maintenance powered by machine learning. Monitor equipment health in real-time, prevent failures before they happen, and optimize energy consumption with intelligent AI insights.

93.75%
Prediction Accuracy
24/7
Real-Time Monitoring
-15%
Energy Reduction

Live Telemetry Dashboard

Offline

Real-time sensor data from industrial equipment. Monitor critical parameters and detect anomalies instantly.

Vibration
--m/s²

Real-time frequency measurement

0/ 100
Normal
Temperature
--°C

Motor bearing temperature

0/ 100
Optimal
Voltage
--V

AC power supply voltage

0/ 100
Stable
Vibration Trend (Live)
Waiting for data...
Temperature Trend (Live)
Waiting for data...

AI Predictive Insights

Offline

Machine learning models analyze sensor data to predict equipment failures before they occur, minimizing downtime and maintenance costs.

M1 - Live Status
Health Score0%
Loading...
RUL: -- Hours
Motor A - Production Line 1
Health Score98%
Healthy
Motor B - Assembly Station
Health Score95%
Healthy

Energy Monitoring & Optimization

Offline

AI-driven insights reduce energy consumption by identifying inefficiencies and optimizing equipment operation schedules.

Energy Consumption Comparison
Live power consumption: Real-time sensor data vs. AI model prediction
Current Power
0.000 W

Live power consumption

Predicted0.000 W
Efficiency0.000%
Peak Demand
-- W

Collecting data...

Technical Architecture

End-to-end IoT stack from sensors to cloud, powered by machine learning for intelligent predictive maintenance.

IoT Sensors

Vibration, temperature, voltage sensors on industrial equipment

Edge Gateway

Raspberry Pi 4B with real-time data preprocessing

AI Processing (Cloud)

TensorFlow LSTM for predictive maintenance analysis

User Dashboard

Real-time monitoring and analytics interface

Technology Stack

TensorFlowPythonAWS IoT CoreRaspberry PiMQTT ProtocolReact Dashboard

Meet the Team

A multidisciplinary team of engineering students working to revolutionize industrial maintenance.

Group 10 Students

Abdullah Munif Alharbi

Energy Engineer

System Design & Simulation Lead

Thamer Abdullah Alharbi​

Energy Engineer

Prototyping Lead

Abdulrahman Alothman​

Energy Engineer

AI & Coding Lead

Thamer Almakrami

Energy Engineer

Documentation Lead

Faculty Advisor

Dr.Nagmeldeen Abdo Mustafa

Faculty Advisor

Energy Engineering Department
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