The goal of this project was to autonomously control part of a tissue mill’s continuous manufacturing process using artificial intelligence and predictive analytics to reduce raw material consumption while maintaining the product quality with specification limit.

Predictive analytics uses a manufacturing machine’s historical data to predict process outcomes. Through machine learning and artificial intelligence, these models continue to learn, adapt and evolve, providing the user with accurate information that can drive better decisions.

This is where the ProcessMiner AutoPilot real-time predictive system comes in and solves the problem, making recommendations and prescribing solutions. In doing this, it minimizes raw materials and reduces costs while maintaining both speed and product quality.

The COVID-19 global pandemic is changing the rules about manufacturing. Our panel of industry experts discusses the ways in which cloud technologies are offering manufacturers a way to deploy, execute and maintain enterprise applications by employees, both on-site and remote.

• Discover how applying advanced data science, analytics and machine learning techniques to your manufacturing process leads to better insights and more focused optimizations to impact ROI
• Learn why companies working under the “new normal” – technology, cloud computing and artificial intelligence are the new game changers for manufacturing

All manufacturers are adjusting to the new normal and with “Social Distancing” in full swing companies are making more aggressive investments in technology to achieve autonomous manufacturing.  This approach accomplishes a couple of important objectives…

Here we will learn the details of data preparation for LSTM models, and build an LSTM Autoencoder for rare-event classification. This post is a continuation of my previous post-Extreme Rare Event Classification using Autoencoders. In the previous post, we talked about the challenges in an extremely rare event data with less than 1% positively labeled data.

In a rare-event problem, we have an unbalanced dataset. Meaning, we have fewer positively labeled samples than negative. In a typical rare-event problem, the positively labeled data are around 5–10% of the total. In an extremely rare event problem, we have less than 1% positively labeled data.

Correlation estimations are commonly used in various data mining applications. In my experience, nonlinear correlations are quite common in various processes. Due to this, nonlinear models, such as SVM, are employed for regression, classification, etc.

The Industrial Revolution paved the way for the life we know today and far surpassed the era of simplistic conveyor belts and heavy manual surveillance. Production lines employ machinery and humans alike. Industries have always stepped up with technological advancement and thereby use a plethora of devices designed and produced to meet specific tasks on factory grounds.

A real-world dataset is provided from the pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x’s) and the event label (y).

Here we will learn an approach to get vector embeddings for string sequences. These embeddings can be used for Clustering and Classification. Sequence modeling has been a challenge. This is because of the inherent un-structuredness of sequence data. Just like texts in Natural Language Processing (NLP), sequences are arbitrary strings.

Researchers are often looking for interesting real-world problems. One major roadblock they face is real-world data. Here we are trying to serve the research community by providing a real-world problem and a dataset. This dataset is created from the pulp-and-paper manufacturing industry. Paper sheets are not the strongest material.

A real-world dataset is provided from the pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x’s) and the event label (y).