Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries

The proper monitoring of quality-related but hard-to-measure variables is currently one of the bottlenecks limiting the safe and efficient operations of industrial processes. This paper proposes a novel ensemble learning algorithm by coordinating global and local Gaussian process regression (GPR) models, and this algorithm is able to capture global and local process behaviours for accurate prediction and timely process monitoring. To further address the deterioration in predictions when using the off-line training and online testing strategy, this paper proposes an adaptive ranking strategy to perform ensemble learning for the sub-GPR models. In this adaptive strategy, we use the moving-window technique to rank and select several of the best sub-model predictions and then average them together to make the final predictions. Last but not least, the least absolute shrinkage and selection operator (Lasso) works together with factor analysis (FA) in a two-step variable selection method to remove under-correlated model input variables in the first stage and to compress over-correlated model input variables in the second stage. The proposed prediction model is validated in two real wastewater treatment plants (WWTPs) with stationary and nonstationary behaviours. The results show that the proposed methodology achieves better performance than other standard methods in the context of their predictions of quality-related variables

Contribuciones de inteligencia artificial aplicada en sistemas industriales

177 p.La dinámica de la sociedad moderna empuja al sector industrial hacia una creciente necesidad de sistemas cada vez más complejos y autónomos, destinada a liberar a los seres humanos de tareas mecánicas, repetitivas y poco gratificantes. Las tecnologías habilitadoras que harán posible esta revolución están disponibles. Y es un hecho que, la Inteligencia Artificial abre un universo de posibilidades para transformar en valor la ingente cantidad de datos existentes. En este campo de investigación, además de las técnicas ya conocidas y ampliamente utilizadas para entrenar modelos, se puede encontrar en la literatura un sinnúmero de variaciones algorítmicas. Sin embargo, esta apuesta por la Inteligencia Artificial no es todavía tangible dentro del sector industrial. Quizás porque estas potentes técnicas han de aterrizarse a la realidad de problemas concretos en industrias reales. Y sin género de dudas, la Inteligencia Artificial Aplicada es clave para ayudar a transformar el ecosistema industrial actual. Urge centrar los esfuerzos en promover estas tecnologías a través de la creación de nuevas herramientas que ejemplifiquen la aplicación de la tecnología del dato y de la Inteligencia Artificial.Este trabajo de Tesis doctoral está centrado, no en la definición de nuevas aportaciones analíticas, sino en la investigación estratégica de las técnicas de Inteligencia Artificial aplicadas al ámbito industrial. Sencillas y entendibles técnicas, capaces de abstraer a la audiencia de las complejas fórmulas matemáticas y de las oscuras cajas negras, aplicadas a la realidad de 3 casos de investigación científica industrial no-supervisados.Inicialmente, se propone la creación de una herramienta para la correcta y equilibrada asignación de consumidores a Fases en la red de Baja Tensión de la Red Eléctrica. En la resolución del problema se aplican algoritmos deoptimización ávaros (greedy) y algoritmos meta-heurísticos (agnósticos al problema y de propósito general) y se describen métricas provenientes de diferentes dominios para medir la calidad de la solución. El concepto común en dichas métricas es el estudio de la complementariedad entre las v curvas de carga (patrones de consumo) de cada consumidor telegestionado de la Línea eléctrica.Posteriormente, se propone un procedimiento para el Control y Supervisión de procesos industriales, donde ciertas variables críticas del proceso son difícilmente medibles. En la resolución del problema, se aplican algoritmos predictivos para inferir la relación entre las variables conocidas y medibles del proceso, y su relación con las variables críticas. El sistema de inferencia propuesto, a través de la correcta secuenciación de técnicas (técnicas de selección de variables relevantes, técnicas de limpieza de datos probabilísticas, técnicas de eliminación de ruidos y redundancias y técnicas de adecuación dinámica a los cambios de comportamiento del proceso), consigue obtener el valor de las variables críticas en tiempo real.Y finalmente, se propone una metodología para la modelización energética de una planta industrial en términos de tasa de producción y de consumos eléctricos individuales (a nivel de máquina) y consumos eléctricos agregados (a nivel de planta). En la resolución del problema se aplican sencillos algoritmos descriptivos y regresivos que permiten reconocer aquellos patrones de comportamiento que justifican el funcionamiento energético de la planta y que permiten detectar las ineficiencias energéticas que no se corresponden con los patrones identificados y descubrir la causa raíz de tales ineficiencias. Se trata de la resolución de un problema de caracterización energética no-supervisado.Asimismo, con objeto de difundir los resultados obtenidos en los casos de investigación industrial se han realizado diversas tareas de diseminación científica (2 artículos de revista y 3 congresos internacionales) y diseminación tecnológica (2 patentes y 1 registro de software).Como reconocimiento a la innovación y calidad de los resultados y aportaciones obtenidas, estas investigaciones aplicadas también han recibido 2 premios de reconocimiento industrial (Best use of Data Science for Industry 4.0 y Research and development of artificial intelligence applied to industrial plants y el reconocimiento de Innobasque como "Caso industrial de referencia". Todos ellos fruto de las diversas innovaciones en el ámbito industrial relacionadas con los resultados de las investigaciones

Building transformative framework for isolation and mitigation of quality defects in multi-station assembly systems using deep learning

The manufacturing industry is undergoing significant transformation towards electrification (e-mobility). This transformation has intensified critical development of new lightweight materials, structures and assembly processes supporting high volume and high variety production of Battery Electric Vehicles (BEVs). As new materials and processes get developed it is crucial to address quality defects detection, prediction, and prevention especially given that e-mobility products interlink quality and safety, for example, assembly of ‘live’ battery systems. These requirements necessitate the development of methodologies that ensure quality requirements of products are satisfied from Job 1. This means ensuring high right-first-time ratio during process design by reducing manual and ineffective trial-and-error process adjustments; and, then continuing this by maintaining near zero-defect manufacturing during production by reducing Mean-Time-to-Detection and Mean-Time-to-Resolution for critical quality defects. Current technologies for isolating and mitigating quality issues provide limited performance within complex manufacturing systems due to (i) limited modelling abilities and lack capabilities to leverage point cloud quality monitoring data provided by recent measurement technologies such as 3D scanners to isolate defects; (ii) extensive dependence on manual expertise to mitigate the isolated defects; and, (iii) lack of integration between data-driven and physics-based models resulting in limited industrial applicability, scalability and interpretability capabilities, hence constitute a significant barrier towards ensuring quality requirements throughout the product lifecycle. The study develops a transformative framework that goes beyond improving the accuracy and performance of current approaches and overcomes fundamental barriers for isolation and mitigation of product shape error quality defects in multi-station assembly systems (MAS). The proposed framework is based on three methodologies which explore MAS: (i) response to quality defects by isolating process parameters (root causes (RCs)) causing unaccepted shape error defects; (ii) correction of the isolated RCs by determining corrective actions (CA) policy to mitigate unaccepted shape error defects; and, (iii) training, scalability and interpretability of (i) and (ii) by establishing closed-loop in-process (CLIP) capability that integrates in-line point cloud data, deep learning approaches of (i) and (ii) and physics-based models to provide comprehensive data-driven defect identification and RC isolation (causality analysis). The developed methodologies include: (i) Object Shape Error Response (OSER) to isolate RCs within single- and multi-station assembly systems (OSER-MAS) by developing Bayesian 3D-convolutional neural network architectures that process point cloud data and are trained using physics-based models and have capabilities to relate complex product shape error patterns to RCs. It quantifies uncertainties and is applicable during the design phase when no quality monitoring data is available. (ii) Object Shape Error Correction (OSEC) to generate CAs that mitigate RCs and simultaneously account for cost and quality key performance indicators (KPIs), MAS reconfigurability, and stochasticity by developing a deep reinforcement learning framework that estimates effective and feasible CAs without manual expertise. (iii) Closed-Loop In-Process (CLIP) to enable industrial adoption of approaches (i) & (ii) by firstly enhancing the scalability by using (a) closed-loop training, and (b) continual/transfer learning. This is important as training deep learning models for a MAS is time-intensive and requires large amounts of labelled data; secondly providing interpretability and transparency for the estimated RCs that drive costly CAs using (c) 3D gradient-based class activation maps. The methods are implemented as independent kernels and then integrated within a transformative framework which is further verified, validated, and benchmarked using industrial-scale automotive sheet metal assembly case studies such as car door and cross-member. They demonstrate 29% better performance for RC isolation and 40% greater effectiveness for CAs than current statistical and engineering-based approaches