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Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection. Department of Education of the Basque Government

Low switching frequency modulation techniques such as Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) or Optimal Pulse Pattern (OPP) are commonly used in medium voltage - high power multilevel converters to improve their efficiency. These techniques require solving non-linear equations in order to calculate the firing angles. These equations can be solved by meta-heuristic optimization algorithms, which depend on a set of hyperparameters that must be properly adjusted to ensure their effectiveness. This paper presents a methodology to tune different meta-heuristic algorithms to fairly compare them and select the best algorithm to solve SHE-PWM/OPP formulation. The methodology is applied considering different meta-heuristic algorithms such as genetic algorithm, differential evolution, harmony search and simulated annealing. It has been validated in SHE-PWM and OPP techniques with different number of firing angles.