Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning

Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases

Industrial Soft Sensor Optimized by Improved PSO: A Deep Representation-Learning Approach

Soft sensors based on deep learning approaches are growing in popularity due to their ability to extract high-level features from training, improving soft sensors’ performance. In the training process of such a deep model, the set of hyperparameters is critical to archive generalization and reliability. However, choosing the training hyperparameters is a complex task. Usually, a random approach defines the set of hyperparameters, which may not be adequate regarding the high number of sets and the soft sensing purposes. This work proposes the RB-PSOSAE, a Representation-Based Particle Swarm Optimization with a modified evaluation function to optimize the hyperparameter set of a Stacked AutoEncoder-based soft sensor. The evaluation function considers the mean square error (MSE) of validation and the representation of the features extracted through mutual information (MI) analysis in the pre-training step. By doing this, the RB-PSOSAE computes hyperparameters capable of supporting the training process to generate models with improved generalization and relevant hidden features. As a result, the proposed method can generate more than 16.4% improvement in RMSE compared to another standard PSO-based method and, in some cases, more than 50% improvement compared to traditional methods applied to the same real-world nonlinear industrial process. Thus, the results demonstrate better prediction performance than traditional and state-of-the-art methods

Controlador adaptativo MRAC em um pêndulo amortecido baseado em modelos matemáticos determinísticos

Os sistemas de controle automático têm grande importância no progresso da Engenharia e da Tecnologia. O controle adaptativo tem a capacidade de modificar seus parâmetros, quando necessário, para diminuir o erro do sistema. Este artigo mostra a aplicação de controle MRAC (Model Reference Adaptive Controller) utilizando a regra MIT, em um pêndulo amortecido, que possui um motor CC com hélice acoplada, como parâmetro de entrada, e o ângulo medido por um acelerômetro, como parâmetro de saída, caracterizando um sistema monovariável. O modelo de referência foi baseado na resposta da curva de saída, utilizando-se os métodos de Mollenkamp e de Smith, considerados métodos determinísticos na área de identificação. Primeiramente, o sistema de controle adaptativo projetado foi simulado com auxílio do Matlab/Simulink, depois os testes experimentais foram realizados no pêndulo amortecido desenvolvido, com resultados experimentais obtidos a partir de uma plataforma de prototipagem Arduino como sistema de aquisição de dados. Os resultados mostram o comportamento do MRAC, cujos parâmetros foram ajustados para obter o comportamento dinâmico o mais próximo possível de um modelo de referência previamente selecionado

Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal