AI-powered Platform Delivers 25 Percent Reduction in Scrap Rates

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.

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.

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).