Renumics GmbH

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Data Curation Checklist for Condition Monitoring (Part 1)

Data collection for condition monitoring has several pitfalls, potentially leading to data that is not suitable for training robust machine learning models. The data problems resulting from the data collection include but are not limited to the presence of failures in the recording equipment, the dominance of specific operating conditions, or mislabeled audio samples. In this article, we will thus help you to ask the right questions and equip you with a checklist you can use when collecting and preparing data for your condition monitoring use case.

Daniel Klitzke

May 24 6 min

Improving Sample Selection in Surrogate Modeling

Selecting samples for training robust surrogate models in simulation can be a real challenge. Active learning-like approaches where samples are selected iteratively can help overcome this challenge. We show how to apply such a procedure to save time and computational resources while making your surrogate model even more robust.

Daniel Klitzke

Mar 24 4 min

Building Robust Visual Inspection Models Using Amazon Lookout for Vision

Building robust models for Visual Inspection in production settings can be a real challenge. Here, cloud services like Amazon Lookout for Vision promise relief for model training but have limitations regarding data curation. This article explores those potential shortcomings and shows how to improve over them to leverage these services to the fullest.

Daniel Klitzke

Mar 15 8 min

Removing Unwanted Bias

Making your data match the real-world data of your use-case is crucial for training a robust machine learning model. This post shows you how to interactively curate your data to adapt your data in an informed manner.

Daniel Klitzke

Feb 16 3 min

Matching Vibroacoustic Test and Simulation Data With Machine Learning

The acoustic and vibrational behavior of a product is a cornerstone of the user experience. This is true for product categories from vacuum…

Stefan Suwelack

Feb 15 5 min

How to Quickly Find and Correct Label Errors

Ensuring label consistency is critical for building robust machine learning models. This post shows you how to achieve label consistency in a data centric way.

Daniel Klitzke

Feb 3 2 min

Training an Acoustic Event Detection System Using Renumics Spotlight

Machine Learning based systems for acoustic event detection typically require a vast amount of training data. Intelligent labeling techniques open up new possibilities for small data scenarios.

Daniel Klitzke

Dec 22 5 min

How Data-Centric ML Helps to Build Reliable Models Fast

ML researchers typically iterate different model architectures on a fixed dataset that they often know very little about. In real use cases, it is often a good idea to focus most efforts on the data and not on the model.

Stefan Suwelack

Dec 9 8 min

Data-Centric AI for Engineering and Manufacturing

Data-centric machine learning is an emerging paradigm. Is this a game-changer for ML in engineering and manufacturing?

Stefan Suwelack

Nov 22 4 min

The AI-Assisted Engineering Canvas

Data-driven methods have arrived and promise to speed up product development. The AI-assisted Engineering Canvas helps to get started.

Stefan Suwelack

Aug 30 8 min

What Is AI-Assisted Design?

Generative ML methods have achieved tremendous success in image and audio processing. Can mechanical engineers benefit from this technology?

Stefan Suwelack

Sep 16 6 min

Use Cases for AI-Assisted Engineering

Machine learning will transform engineering work. But how does this process look like and where will it start?

Stefan Suwelack

Feb 24 6 min

What Is AI-Assisted Engineering ?

Engineers spend a lot of time on manual routine tasks. Novel machine learning techniques promise to change that.

Stefan Suwelack

Jan 30 5 min