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MLOps 7 ay MLOps Engineer & System Architect

Smart Factory IoT Dashboard

IoT + AI platform that analyzes factory sensor data in real-time, providing predictive maintenance and energy optimization within Industry 4.0 scope.

%70
Downtime Reduction
%22
Energy Savings
%92
Prediction Accuracy
%45
Maintenance Cost Reduction

Challenge

Managing high-frequency data streams from 500+ sensors, building a generalized failure prediction model for different machine types, and synchronizing data between edge and cloud were the main challenges.

Solution

Achieved efficient data collection with MQTT broker and time series optimization with InfluxDB. Trained machine-type-specific models with transfer learning. Made critical decisions locally with edge computing.

Highlights

1

500+ IoT sensor integration and data pipeline

2

Predictive maintenance model with LSTM + Prophet

3

Energy optimization with Genetic Algorithm

4

Scalable deployment with Kubernetes

Technology Stack

Python
LSTM
Prophet
InfluxDB
Grafana
MQTT
Docker
Kubernetes

About the Project

This platform analyzes data from 500+ IoT sensors in a manufacturing facility in real-time, predicting machine failures 72 hours in advance.

Technical Architecture

  • Time Series analysis models (LSTM + Prophet)
  • MQTT &
  • Grafana-based real-time dashboard
  • Predictive maintenance model
  • Energy consumption optimization (Genetic Algorithm)
  • Anomaly detection and automated alarm system

    • Results

    Achieved 70% reduction in unplanned downtime, 22% savings in energy costs, and 45% decrease in maintenance costs.