IoT Signal Analysis for Agricultural Machinery

Sat, 15 May 2021 01:15:44 +0200

I recently had the opportunity to work on a project that involved designing and developing a data pipeline with signal analysis for a client’s milling machines. As part of the project, I led a team of 4 developers to ensure the successful deployment of the project in the production environment.

During the project, I was able to profile and improve the CPU (x5 improvement) and memory (50% memory reduction) usage of the pipeline, resulting in improved efficiency of the milling machines. Our solution detected and analyzed Internet of Things (IoT) signals emitted by the milling machines.

The project aimed to create a system that could track, monitor, and analyze the signals to detect any anomalies that might indicate potential issues. The system was also meant to explore the signals and provide insights into the performance of the machines.

We developed a Python system and deployed it on the edge device using Azure Pipelines to achieve this. Our solution was highly scalable and efficient, providing real-time analysis and insights into the performance of the milling machines.

This project was an excellent opportunity to showcase the power of data science and machine learning in improving the efficiency and performance of industrial processes. By leveraging IoT signals and data analysis techniques, we created a solution that significantly impacted our client’s business outcomes. My experience in data science and machine learning has enabled me to contribute to innovative solutions that drive business outcomes.

Vehicle information analysis

Sun, 15 Dec 2019 01:15:44 +0200

As a data scientist, I had the opportunity to work on a project that involved analyzing complex vehicle information standards such as Open Diagnostic data eXchange (ODX). Most modern automotive brands use the ODX format to define essential information associated with their vehicles, including service intervals, maintenance schedules, and possible failures that can lead to serious technical problems. However, due to its complexity, understanding the ODX format can be challenging, requiring a deep understanding of the entire content structure, composed of many levels, each highly specialized and varying according to country and manufacturer.

The project was developed to improve the creation process of Periodic Technical Inspections (PTI) and to provide a better understanding of the ODX format. As part of the project, I offered the following: An extensive exploratory analysis. Visualizations of different parts of the ODX format. A study of the ODX format itself.

By leveraging my data analysis and visualization expertise, I helped the client design its process of utilizing the ODX format and create a PTI process using ODX information. Our solutions enabled the client to understand the ODX structure better and use it more effectively, resulting in improved PTI creation processes.

This project was an excellent opportunity to showcase the power of data science and machine learning in improving the efficiency and performance of automotive industry processes. By leveraging data analysis and visualization techniques, we created a solution that significantly impacted the client’s business outcomes. My experience in data science and machine learning has enabled me to contribute to innovative solutions that drive business outcomes.

Exploratory Data Analysis | AI Solutions Łukasz Augustyniak

IoT Signal Analysis for Agricultural Machinery

Vehicle information analysis