Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant

U ovom je radu razvijena umjetna neuronska mreža (ANN) za brzo pronalaženje grešaka na pneumatskom sustavu. Podaci su prikupljeni i procijenjeni smatrajući da sustav radi savršeno, a greške su unaprijed predviđene. Greške uključuju manjak boce, ne funkcioniranje cilindra B za stavljanje poklopca, neispravni cilindar C za stavljanje poklopca na boce, nedovoljan tlak zraka, voda se ne puni i nizak tlak zraka. Tijekom postupka prikupljeni su signali šest senzora te je za ANN kodirano 18 najkarakterističnijih obilježja podataka. Primijenjene su dvije različite umjetne neuronske mreže (ANN) za interpretaciju kodiranih signala. Umjetne neuronske mreže testirane u ispitivanju bile su "learning vector quantization (LVQ)" i "radial basis network (RBN)". Ustanovilo se da te dvije vrste umjetnih neuronskih mreža dobro funkcioniraju u primijenjenim postupcima u sustavu u kojem se sekvencijski podaci ponavljaju.In this study, an Artificial Neural Network (ANN) is developed to find faults rapidly on a pneumatic system. The data were saved and evaluated considering system is working perfectly and faults are predetermined. These faults include having no bottle, a nonworking cap closing cylinder B, a nonworking bottle cap closing cylinder C, insufficient air pressure, water not filling and low air pressure faults. The signals of six sensors were collected during the entire sequence and the 18 most descriptive features of the data were encoded to present to the ANNs. Two different ANNs were applied for interpretation of the encoded signals. The ANNs tested in the study were learning vector quantization (LVQ) and radial basis network (RBN). The performance of LVQ and RBN was found to be fine with the presented procedures for a system having very repetitive sequential data

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Diagnóstico de Procesos Industriales Mediante Predicción de Estados Funcionales con Inteligencia Artificial

Este artículo presenta el diseño de una estrategia inteligente, para el diagnóstico automático de procesos industriales mediante la predicción con Redes Neuronales Artificiales (RNAs) y clasificación difusa. Para diseñar la estrategia de diagnóstico se utilizó información histórica del proceso. La clasificación fue implementada como herramienta para el agrupamiento difuso de patrones. Las RNAs de configuración multicapa fueron entrenadas para predecir los estados funcionales del proceso. Las salidas en la etapa de predicción son las entradas del clasificador. En el esquema de diagnóstico propuesto la estimación de los estados funcionales es presentada a los operarios de los procesos, como información futura para generar las acciones preventivas antes de la transición hacia un estado de falla. La estrategia propuesta fue implementada sobre un sistema de control convencional; y sobre un sistema de producción de aire medicinal

Diagnóstico de Procesos Industriales Mediante Predicción de Estados Funcionales con Inteligencia Artificial

Este artículo presenta el diseño de una estrategia inteligente, para el diagnóstico automático de procesos industriales mediante la predicción con Redes Neuronales Artificiales (RNAs) y clasificación difusa. Para diseñar la estrategia de diagnóstico se utilizó información histórica del proceso. La clasificación fue implementada como herramienta para el agrupamiento difuso de patrones. Las RNAs de configuración multicapa fueron entrenadas para predecir los estados funcionales del proceso. Las salidas en la etapa de predicción son las entradas del clasificador. En el esquema de diagnóstico propuesto la estimación de los estados funcionales es presentada a los operarios de los procesos, como información futura para generar las acciones preventivas antes de la transición hacia un estado de falla. La estrategia propuesta fue implementada sobre un sistema de control convencional; y sobre un sistema de producción de aire medicinal

Diagnóstico de Procesos Industriales Mediante Predicción de Estados Funcionales con Inteligencia Artificial

Este artículo presenta el diseño de una estrategia inteligente, para el diagnóstico automático de procesos industriales mediante la predicción con Redes Neuronales Artificiales (RNAs) y clasificación difusa. Para diseñar la estrategia de diagnóstico se utilizó información histórica del proceso. La clasificación fue implementada como herramienta para el agrupamiento difuso de patrones. Las RNAs de configuración multicapa fueron entrenadas para predecir los estados funcionales del proceso. Las salidas en la etapa de predicción son las entradas del clasificador. En el esquema de diagnóstico propuesto la estimación de los estados funcionales es presentada a los operarios de los procesos, como información futura para generar las acciones preventivas antes de la transición hacia un estado de falla. La estrategia propuesta fue implementada sobre un sistema de control convencional; y sobre un sistema de producción de aire medicinal

Use, Operation and Maintenance of Renewable Energy Systems:Experiences and Future Approaches

The aim of this book is to put the reader in contact with real experiences, current and future trends in the context of the use, exploitation and maintenance of renewable energy systems around the world. Today the constant increase of production plants of renewable energy is guided by important social, economical, environmental and technical considerations. The substitution of traditional methods of energy production is a challenge in the current context. New strategies of exploitation, new uses of energy and new maintenance procedures are emerging naturally as isolated actions for solving the integration of these new aspects in the current systems of energy production. This book puts together different experiences in order to be a valuable instrument of reference to take into account when a system of renewable energy production is in operation

Expert systems in on-line process control and fault diagnosis

In this research expert systems for on-line process control and fault diagnosis have been investigated and the majority of the research is on using expert systems in on-line process fault diagnosis. Several on-line expert systems, including a rule based controller and several fault diagnosis systems, have been developed in this research and are reported in this thesis. The research results obtained demonstrate that rule based controllers can be used in situations where mathematical models for the controlled process cannot be obtained or are very difficult to obtain. The research on on-line fault diagnosis emphasises deep knowledge based approaches. Two avenues in deep knowledge based approaches, namely causal search and qualitative modelling based diagnosis, have been investigated. In the approach of causal search the research results reveal that diagnostic efficiency can be achieved through structural decomposition. A systematic approach for developing diagnostic rules based on structural decomposition is presented in this thesis. Much of the research has been done on qualitative model based fault diagnosis. A qualitative reasoning method which utilizes knowledge on the quantitative relations among variables to reduce ambiguity and can cope with a wider range of situations than Raiman's Order of Magnitude Reasoning is investigated. In the qualitative model based diagnosis the function of the qualitative model is to predict the behaviour of the process under various hypotheses and, therefore, to verify these hypotheses. Further research concerning self-reasoning has been done for the qualitative model based diagnosis approach. Self-reasoning is achieved by backward tracing through the model of the diagnosis system and makes this diagnosis system more intelligent. Self-learning of heuristic rules based on qualitative modelling is investigated and heuristic rules can add efficiency to model based diagnosis. During investigating self-learning of heuristic rules, the good learning property of neural networks is recognised and, neural networks based on-line fault diagnoses are also investigated. The research results reveal that neural networks based diagnosis systems are easy to develop and perform robustly provided that the training data are available