Components of the Platform

1. Simulated Sensor Data Generation: Simulates machinery sensor data (e.g., temperature, pressure, and vibration levels).

2. SQL Database: Stores sensor data using SQLite for simplicity.

3. Data Analytics: Python script fetches data from the database and processes it for patterns.

4. Visualization: Generates visualizations (using Matplotlib/Seaborn) for operational insights.

5. Automated Report Generation: Exports data summaries and plots to a PDF report.

Explanation Simulated Data: Random values for temperature, pressure, and vibration simulate real-world sensor readings.

Database: Data is stored in an SQLite database with columns for timestamp, temperature, pressure, and vibration.

Analysis: Data is fetched and analyzed using Pandas. Summary statistics like mean, standard deviation, and percentiles are calculated.

Visualization: Time series trends for temperature, pressure, and vibration are plotted using Matplotlib. The plot is saved as an image (sensor_trends.png).

Automated Report: FPDF is used to create a PDF report containing: Summary statistics. Sensor data trend visualization.

Expected Output -SQLite Database: Stores 100 rows of simulated sensor data.

-Console Output*: Displays fetched data and summary statistics.

-Plots: Line plots for temperature, pressure, and vibration trends.

-PDF Report: Machine_Data_Analysis_Report.pdf with analysis and visualization.