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In system monitoring, automatic fault diagnosis seeks to infer the systems'state based on sensor readings, e.g., through machine learning models. In thiscontext, it is of key importance that, based on historical data, these systemsare able to generalize to incoming data. In parallel, many factors may inducechanges in the data probability distribution, hindering the possibility of suchmodels to generalize. In this sense, domain adaptation is an importantframework for adapting models to different probability distributions. In thispaper, we propose a new benchmark, based on the Tennessee Eastman Process ofDowns and Vogel (1993), for benchmarking domain adaptation methods in thecontext of chemical processes. Besides describing the process, and itsrelevance for domain adaptation, we describe a series of data processing stepsfor reproducing our benchmark. We then test 11 domain adaptation strategies onthis novel benchmark, showing that optimal transport-based techniquesoutperform other strategies.

Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process