Exploratory Data Analysis (EDA) for Fault Diagnosis using Machine Learning
#machinelearning #faultdetection #dataanalysis #exploratorydataanalysis
#conditionmonitoring #predictivemaintenance
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In this video, we will dive into the first step of fault diagnosis using machine learning - exploratory data analysis (EDA). We will be using the Tennessee Eastman dataset, which is a widely used benchmark dataset for fault diagnosis. The dataset contains process data for a simulated chemical process with various types of faults.
In this tutorial, we will start by discussing the importance of exploratory data analysis in machine learning and its role in fault diagnosis. We will then use Python libraries like NumPy, Pandas, Matplotlib, and Seaborn to perform EDA on the Tennessee Eastman dataset. We will explore the data, identify patterns and anomalies, and visualize the data using various plots and charts.
By the end of this video, you will have a good understanding of exploratory data analysis and how it can be used to gain insights into complex datasets. This will set the foundation for the subsequent videos in this series, where we will train various machine learning models to diagnose faults in the Tennessee Eastman dataset.
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GitHub (Jupyter Notebook file of this video) - https://github.com/mohan696matlab/Pro...
dataset link - https://www.kaggle.com/datasets/averk...
Full Playlist - • Machine Learning for Fault Diagnosis:...
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Hello everyone! My name is Mohan, and I'm currently pursuing my PhD in artificial intelligence. My research focuses on fault diagnosis of green hydrogen multi-source hybrid systems, which is an exciting field that contributes to the development of sustainable energy technologies.
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