In a blog post covering seven trends to look out for in 2022, Daniel Fallmann, founder and CEO, shared,

“The possibilities of augmented reality in the business sector go far beyond the classic data glasses. In principle, all digital entities can be enriched with additional information, whether it be the radiological image of a bone or the 3D model of a component - and this applies to a wide variety of output devices. Here, AI-based systems use methods such as machine and deep learning as well as speech recognition to ensure not only that the database required for augmentation is available, but also that the relevant information and content is provided proactively and in the right context.”

Data augmentation adds modified copies of existing data to increase the amount of data in a given data set.

When training machine learning models, data augmentation is a popular technique to enhance the performance of deep learning neural networks. The amount of data available plays a critical role in how accurately and quickly a training model can learn – this leads to more robust deep learning.

Data Augmentation in Maintenance

Maintenance has been a field that has significantly benefited from data augmentation. Let’s have a look at some of the reasons why.

Machine failures cost a business a hefty amount of money, leading to downtime and labor costs.

With data augmentation, the scope of what a maintenance professional can visualize regarding a specific component rather than what they would see from the standalone natural image is much more robust. With fewer training images, this is an essential technique for workers to extract as much meaning as possible from a machine failure – why did this occur, and what is the best route to fix this and prevent it from happening in the future.

As augmented information gives the models more data to learn from, patterns related to machine failures can be identified. A 360-degree view of a part, component, or model can be graphically represented on a dashboard right at the user’s fingertips.

Mindbreeze InSpire for maintenance enables companies to:

• Identify the optimal time for maintenance
• Safeguard the production capability
• Boost productivity
• Maximize profitability
• Optimize capacity utilization and machine performance

Popular image classification and segmentation techniques used for image data augmentation include cropping, rotating, re-scaling, vertical and horizontal flipping, zooming, contrast changing, and brightness modifying.

Data Augmentation and Natural Language Processing (NLP)

Images are one thing, but data augmentation for text classification and natural language processing (NLP) is an entirely different story and a bit more complex. Sentence structure, word use, synonyms, and other languages all play a role in how rich the human language is.

A sentence with the same meaning can be worded and structured in various ways, making learning difficult for NLP models.

Thanks to data augmentation, a model can be manipulated and fed a much larger data set, improving the output significantly. This level of accuracy and bias reduction can increase chatbot efficiency, search result relevancy, and more.