Autoencoder-based semantic novelty detection: towards dependable AI-based systems

Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from the literature by minimizing false negatives

Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing

[ES] Los avances tecnológicos en general, y en el ámbito de la industria en particular, conllevan el desarrollo y optimización de las actividades que en ella tienen lugar. Para alcanzar este objetivo, resulta de vital importancia detectar cualquier tipo de anomalía en su fase más incipiente, contribuyendo, entre otros, al ahorro energético y económico, y a una reducción del impacto ambiental. En un contexto en el que se fomenta la reducción de emisión de gases contaminantes, las energías alternativas, especialmente la energía eólica, juegan un papel crucial. En la fabricación de las palas de aerogenerador se recurre comúnmente a materiales de tipo bicomponente, obtenidos a través del mezclado de dos substancias primarias. En la presente investigación se evalúan distintas técnicas inteligentes de clasificación one-class para detectar anomalías en un sistema de mezclado para la obtención de materiales bicomponente empleados en la elaboración de palas de aerogenerador. Para lograr los modelos[EN] Technological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmental impact reduction. In a context where the reduction of pollution gasses emission is promoted, the use of alternative energies, specially the wind energy, plays a key role. The wind generator blades are usually manufactured from bicomponent material, obtained from the mixture of two dierent primary components. The present research assesses dierent one-class intelligent techniques to perform anomaly detection on a bicomponent mixing system used on the wind generator manufacturing. To perform the anomaly detection, the intelligent models were obtained from real dataset recorded during the right operation of a bicomponent mixing plant. 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