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IoT-based Manufacturing Data Analysis and Anomaly Detection Master Class

Are you missing anomalies in complex manufacturing site data? I am sharing my know-how on how to accurately detect them using autoencoders with real-world IoT data.

1 learners are taking this course

Level Intermediate

Course period Unlimited

IoT
IoT
analytics
analytics
timeserieschart
timeserieschart
encoder-decoder
encoder-decoder
IoT
IoT
analytics
analytics
timeserieschart
timeserieschart
encoder-decoder
encoder-decoder

What you will gain after the course

  • Design and Implementation of an Anomaly Detection System Through IoT-Based Manufacturing Data Analysis

  • Latent Space Learning and Unsupervised Learning-based Pattern Analysis of High-Dimensional Time-Series Data

  • Development of Autoencoder-based Prediction and Diagnosis Solutions in Smart Factory Environments

This course is a masterclass for professionals seeking to enhance their capabilities in intelligent manufacturing data analysis and anomaly detection using Autoencoders, a core technology of the Industry 4.0 era. It covers in-depth theoretical backgrounds such as the basic concepts of Autoencoders, their operating principles, and the manifold hypothesis. In particular, we will focus on mechanisms for effectively processing high-dimensional time-series and unstructured data in the manufacturing domain, identifying latent spaces by learning from vast amounts of unlabeled normal data, and detecting heterogeneous signals that deviate from learned patterns as anomalies. Participants will learn design and implementation strategies for Autoencoder-based solutions through various types, such as Stacked Autoencoders and 1D-CNN Autoencoders, and real-world application cases including steel rolling processes, industrial motor diagnostics, and virtual sensor utilization. By encompassing data preprocessing, solutions for interpretability issues, and future trends, this course provides practical knowledge and skills to contribute to maximizing production efficiency and achieving zero defects on the manufacturing floor.

Recommended for
these people

Who is this course right for?

  • Engineers seeking to improve production efficiency through IoT and manufacturing data analysis

  • A developer aiming to develop AI-based anomaly detection solutions in a smart factory environment

  • Manufacturing field experts who want to strengthen data-driven decision-making capabilities in the Industry 4.0 era

Need to know before starting?

  • Basic knowledge of Python programming and deep learning frameworks (TensorFlow, PyTorch)

  • Understanding of basic machine learning concepts and neural network structures

  • Basic domain knowledge of time-series data analysis and manufacturing processes

Hello
This is mj

63

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4

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4.5

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Curriculum

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26 lectures ∙ (2hr 59min)

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