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Advocating for a sustainable, resilient and human-centric industry, the threepillars of Industry 5.0 call for an increased understanding of industrialprocesses and manufacturing systems, as well as their energy sustainability.One of the most fundamental elements of comprehension is knowing when thesystems are operated, as this is key to locating energy intensive subsystemsand operations. Such knowledge is often lacking in practice. Activationstatuses can be recovered from sensor data though. Some non-intrusive sensors(accelerometers, current sensors, etc.) acquire mixed signals containinginformation about multiple actuators at once. Despite their low cost as regardsthe fleet of systems they monitor, additional signal processing is required toextract the individual activation sequences. To that end, sparse regressiontechniques can extract leading dynamics in sequential data. Notoriousdictionary learning algorithms have proven effective in this regard. This paperconsiders different industrial settings in which the identification of binarysubsystem activation sequences is sought. In this context, it is assumed thateach sensor measures an extensive physical property, source signals areperiodic, quasi-stationary and independent, albeit these signals may becorrelated and their noise distribution is arbitrary. Existing methods eitherrestrict these assumptions, e.g., by imposing orthogonality or noisecharacteristics, or lift them using additional assumptions, typically usingnonlinear transforms.

Unsupervised Complex Semi-Binary Matrix Factorization for Activation Sequence Recovery of Quasi-Stationary Sources