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

Hierarchical Control of the ATLAS Experiment

Control systems at High Energy Physics (HEP) experiments are becoming increasingly complex mainly due to the size, complexity and data volume associated to the front-end instrumentation. In particular, this becomes visible for the ATLAS experiment at the LHC accelerator at CERN. ATLAS will be the largest particle detector ever built, result of an international collaboration of more than 150 institutes. The experiment is composed of 9 different specialized sub-detectors that perform different tasks and have different requirements for operation. The system in charge of the safe and coherent operation of the whole experiment is called Detector Control System (DCS). This thesis presents the integration of the ATLAS DCS into a global control tree following the natural segmentation of the experiment into sub-detectors and smaller sub-systems. The integration of the many different systems composing the DCS includes issues such as: back-end organization, process model identification, fault detection, synchronization with external systems, automation of processes and supervisory control. Distributed control modeling is applied to the widely distributed devices that coexist in ATLAS. Thus, control is achieved by means of many distributed, autonomous and co-operative entities that are hierarchically organized and follow a finite-state machine logic. The key to integration of these systems lies in the so called Finite State Machine tool (FSM), which is based on two main enabling technologies: a SCADA product, and the State Manager Interface (SMI++) toolkit. The SMI++ toolkit has been already used with success in two previous HEP experiments providing functionality such as: an object-oriented language, a finite-state machine logic, an interface to develop expert systems, and a platform-independent communication protocol. This functionality is then used at all levels of the experiment operation process, ranging from the overall supervision down to device integration, enabling the overall sequencing and automation of the experiment. Although the experience gained in the past is an important input for the design of the detector's control hierarchy, further requirements arose due to the complexity and size of ATLAS. In total, around 200.000 channels will be supervised by the DCS and the final control tree will be hundreds of times bigger than any of the antecedents. Thus, in order to apply a hierarchical control model to the ATLAS DCS, a common approach has been proposed to ensure homogeneity between the large-scale distributed software ensembles of sub-detectors. A standard architecture and a human interface have been defined with emphasis on the early detection, monitoring and diagnosis of faults based on a dynamic fault-data mechanism. This mechanism relies on two parallel communication paths that manage the faults while providing a clear description of the detector conditions. The DCS information is split and handled by different types of SMI++ objects; whilst one path of objects manages the operational mode of the system, the other is to handle eventual faults. The proposed strategy has been validated through many different tests with positive results in both functionality and performance. This strategy has been successfully implemented and constitutes the ATLAS standard to build the global control tree. During the operation of the experiment, the DCS, responsible for the detector operation, must be synchronized with the data acquisition system which is in charge of the physics data taking process. The interaction between both systems has so far been limited, but becomes increasingly important as the detector nears completion. A prototype implementation, ready to be used during the sub-detector integration, has achieved data reconciliation by mapping the different segments of the data acquisition system into the DCS control tree. The adopted solution allows the data acquisition control applications to command different DCS sections independently and prevents incorrect physics data taking caused by a failure in a detector part. Finally, the human-machine interface presents and controls the DCS data in the ATLAS control room. The main challenges faced during the design and development phases were: how to support the operator in controlling this large system, how to maintain integration across many displays, and how to provide an effective navigation. These issues have been solved by combining the functionalities provided by both, the SCADA product and the FSM tool. The control hierarchy provides an intuitive structure for the organization of many different displays that are needed for the visualization of the experiment conditions. Each node in the tree represents a workspace that contains the functional information associated with its abstraction level within the hierarchy. By means of an effective navigation, any workspace of the control tree is accessible by the operator or detector expert within a common human interface layout. The interface is modular and flexible enough to be accommodated to new operational scenarios, fulfil the necessities of the different kind of users and facilitate the maintenance during the long lifetime of the detector of up to 20 years. The interface is in use since several months, and the sub-detector's control hierarchies, together with their associated displays, are currently being integrated into the common human-machine interface

An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

Cognitive Buildings

Cognitive building is a pioneering topic envisioning the future of our built environment. The concept of "cognitive" provides a paradigm shift that steps from the static concept of the building as a container of human activities towards the modernist vision of "machine à habiter" of Le Corbusier, where the technological content adds the capability of learning from users' behavior and environmental variables to adapt itself to achieve major goals such as user comfort, energy-saving, flexible functionality, high durability, and good maintainability. The concept is based on digital frameworks and IoT networks towards the concept of a smart city