Iot open-source architecture for the maintenance of building facilities

none6noThe introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to moni-tor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.openVilla V.; Naticchia B.; Bruno G.; Aliev K.; Piantanida P.; Antonelli D.Villa, V.; Naticchia, B.; Bruno, G.; Aliev, K.; Piantanida, P.; Antonelli, D

Machine Learning Framework for the Sustainable Maintenance of Building Facilities

The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning (HVAC) system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and forecast anomalies in the 3D model of the fan coil. The faults were then predicted using a classification model, and the results of the models are introduced. Finally, the IoT data from the building facility and the predicted values of the ML models are visualized in the building facility’s BIM model and the real-time monitoring dashboard, respectively

Sistemi integrati BIM-GIS nella progettazione di edilizia ospedaliera ad alta efficienza energetica

The interoperability of the tools for managing and controlling the design process is one of the themes on which research and innovations are focussed in the field of BIM (Building Information Modelling) systems. A strategic objective of the STREAMER research, co-funded by the European Union within the context of the Seventh Framework Programme, is that of defining the tools and methods of designing hospital buildings that allow for a 50% reduction in energy consumption and emissions in large healthcare districts. Contributing to the achievement of this result is the creation of integrated tools, based on BIM and GIS systems, capable of providing an effective backup to the decisions of the various subjects involved in the project and the management of hospital complexes

IoT*(Ambisense): Smart environment monitoring using LoRa

In this work, IoT* (AmbiSense), we present our developed IoT system as a solution for Building and Energy Management using visualization tools to identify heuristics and create automatic savings. Our developed prototypes communicate using LoRa, one of the latest IoT technologies, and are composed of a set of battery-operated sensors tied to a System on Chip. These sensors acquire environmental data such as temperature, humidity, luminosity, air quality, and also motion. For small to medium-size buildings where system management is possible, a multiplatform dashboard provides visualization templates with real-time data, allowing to identify patterns and extract heuristics that lead to savings using a set of pre-defined actions or manual intervention. LoBEMS (LoRa Building and Energy Management System), was validated in a kindergarten school during a three-year period. As an outcome, the evaluation of the proposed platform resulted in a 20% energy saving and a major improvement of the environment quality and comfort inside the school. For larger buildings where system management is not possible, we created a 3D visualization tool, that presents the system collected data and warnings in an interactive model of the building. This scenario was validated at ISCTE-IUL University Campus, where it was necessary to introduce the community interaction to achieve savings. As a requested application case, our system was also validated at the University Data Center, where the system templates were used to detect anomalies and suggest changes. Our flexible system approach can easily be deployed to any building facility without requiring large investments or complex system deployments.Nesta dissertação de mestrado, IoT * (AmbiSense), é apresentado um sistema IoT desenvolvido como uma solução para Gestão de Edifícios e Energia recorrendo a ferramentas de visualização para identificar heurísticas e criar poupanças automáticas. Os protótipos desenvolvidos comunicam utilizando LoRa, e são compostos por um conjunto de sensores ligados a um microcontrolador alimentado por bateria. Os sensores adquirem dados como temperatura, humidade, luminosidade, qualidade do ar e movimento. Para edifícios de pequena e média dimensão onde a gestão do sistema é possível, um dashboard fornece templates de visualização com dados em tempo real, permitindo extrair heurísticas, que introduzem poupanças através de um conjunto de ações predefinidas ou intervenção manual. O sistema LoBEMS (LoRa Building and Energy Management System), foi validado numa escola local durante um período de três anos. A avaliação do sistema resultou numa poupança de energia de 20% e uma melhoria significativa da qualidade do ambiente e conforto no interior da escola. Para edifícios de maior dimensão onde a gestão do sistema não é possível, criámos uma ferramenta de visualização 3D, que apresenta os dados e alertas do sistema, num modelo interativo do edifício. Este cenário foi validado no campus do ISCTE-IUL, onde foi necessária a interação da Comunidade para obter poupanças. Foi nos também solicitada uma validação do sistema no centro de dados da Universidade, onde os templates do sistema foram utilizados para detetar anomalias e sugerir alterações. A flexibilidade do sistema permite a sua implementação em qualquer edifício, sem exigir um grande investimento ou implementações complexas