Title: Sensors Data Analysis for PetrSU Course Project

Introduction:

Welcome to the official GitHub repository for our university course project on Sensors Data Analysis! This project is part of our academic curriculum, aimed at exploring the world of sensor signal data and applying data analysis techniques to gain valuable insights. Here, you'll find all the resources, code, documentation, and findings related to our exploration and analysis.

Project Overview:

In this project, we have recieved real data from local factory from such sensors as Accelerometer. The main aim was to undestand the damage of the factory equipment in real-time by the data from sensors.Our tasks were to understand the patterns, trends, and anomalies present in the sensor data, enabling us to make data-driven decisions and draw meaningful conclusions.

Features:

• Data Preprocessing: Cleaning, transforming, and organizing raw sensor signal data to make it suitable for analysis.
• Exploratory Data Analysis: Uncovering patterns, distributions, and correlations in the sensor data.
• Visualization: Priting scalogram and signal spectrum by the plotly library
• Machine Learning Techniques: Exploring machine learning models for predicting sensor values and anomaly detection.
• Data Interpretation: Interpreting results and drawing meaningful conclusions from the analysis.

Project Structure:

• data: Contains the raw and processed sensor data used for the analysis.
• notebooks: Jupyter notebooks detailing the step-by-step analysis and code implementation.
• scripts: Python scripts for data preprocessing, statistical analysis, and machine learning models.
• documentation: Project report, presentation slides, and other related documents.
• build: Project working folder
• template: Template of project output

Getting Started:

• git clone https://github.com/your-username/sensors-data-analysis.git
• pip install -r requirements.txt
• python3 .scripts/create_executable.py
• cd dist
• ./MeasureAnalysis Explore the notebooks in the notebooks directory to follow the analysis and reproduce the results. Use the scripts in the scripts directory to apply specific data preprocessing or modeling tasks.

License:

This project is licensed under the MIT License, allowing you to use, modify, and distribute the code freely while giving appropriate credit to the contributors.

Authors:

• Olga Masaeva
• Fyodor Bykov
• Denis Karpovsky

Thank you for visiting our project repository! We hope our work on sensors data analysis inspires you and contributes to your learning and understanding of this exciting field.