The Arc Loss Challenge: A Novel Industrial Benchmark for Process Analytics and Machine Learning

Spartan Controls is proud to share the significant contribution of our very own Spartan Lee D. Rippon to the co-authorship of the Arc Loss Challenge: A Novel Industrial Benchmark for Process Analytics and Machine Learning. Lee collaborated with Ibrahim Yousef, Carole Prévose, Sirish L. Shad, and R. Bhushan Gopaluni in this research, which delves into the rapid advancements in data-driven process monitoring. This paper introduces a benchmark challenge called the "arc loss challenge." The challenge is designed to enhance the evaluation and comparison of learning algorithms and overall data preprocessing workflows for data-driven process monitoring, specifically focusing on fault detection and diagnosis in large-scale industrial operations, such as mining and pyrometallurgy.
 

Rapid development in data-driven process monitoring has provided a rich selection of models and data preprocessing strategies for applications such as fault detection and diagnosis. However, the development, comparison, and selection of process monitoring algorithms can become complicated and unnecessarily onerous. As a result, numerous publicly available benchmark datasets have emerged in the literature. Unfortunately, benchmark literature often suffers from problems such as low fidelity, inconsistent usage, and lack of transparency. This paper presents a benchmark challenge based on a large-scale industrial dataset that aims to enhance the evaluation and comparison of learning algorithms and overall data preprocessing workflows. We introduce the arc loss challenge, a machine learning benchmark with data from a large-scale mining and pyrometallurgy operation. By providing a supervised learning challenge based on large quantities of raw industrial process data with transparent and consistent evaluation procedures, the arc loss challenge is a unique contribution to fault detection benchmarking.