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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.
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