45 research outputs found
Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems
Globally, the buildings sector accounts for 30% of the energy consumption and
more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and
Air Conditioning (HVAC) system is the most extensively operated component and it is
responsible alone for 40% of the final building energy usage. HVAC systems are used
to provide healthy and comfortable indoor conditions, and their main objective is to
maintain the thermal comfort of occupants with minimum energy usage.
HVAC systems include a considerable number of sensors, controlled actuators, and
other components. They are at risk of malfunctioning or failure resulting in reduced efficiency,
potential interference with the execution of supervision schemes, and equipment
deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve
their reliability, efficiency, and performance, and to provide preventive maintenance.
In this thesis work, two neural network-based methods are proposed for sensor and
actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised
sensor data validation and fault diagnosis method using an Auto-Associative Neural
Network (AANN) is developed. The method is based on the implementation of Nonlinear
Principal Component Analysis (NPCA) using a Back-Propagation Neural Network
(BPNN) and it demonstrates notable capability in sensor fault and inaccuracy
correction, measurement noise reduction, missing sensor data replacement, and in both
single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks
(CNNs) is developed for single actuator faults. It is based a data transformation in
which the 1-dimensional data are configured into a 2-dimensional representation without
the use of advanced signal processing techniques. The CNN-based actuator fault
diagnosis approach demonstrates improved performance capability compared with the
commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and
standard Neural Networks).
The presented schemes are compared with other commonly used HVAC fault diagnosis
methods for benchmarking and they are proven to be superior, effective, accurate,
and reliable. The proposed approaches can be applied to large-scale buildings with
additional zones
GRU-based denoising autoencoder for detection and clustering of unknown single and concurrent faults during system integration testing of automotive software systems
Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review
This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository
Real-time detection of uncalibrated sensors using Neural Networks
Nowadays, sensors play a major role in several contexts like science,
industry and daily life which benefit of their use. However, the retrieved
information must be reliable. Anomalies in the behavior of sensors can give
rise to critical consequences such as ruining a scientific project or
jeopardizing the quality of the production in industrial production lines. One
of the more subtle kind of anomalies are uncalibrations. An uncalibration is
said to take place when the sensor is not adjusted or standardized by
calibration according to a ground truth value. In this work, an online
machine-learning based uncalibration detector for temperature, humidity and
pressure sensors was developed. This solution integrates an Artificial Neural
Network as main component which learns from the behavior of the sensors under
calibrated conditions. Then, after trained and deployed, it detects
uncalibrations once they take place. The obtained results show that the
proposed solution is able to detect uncalibrations for deviation values of 0.25
degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to
different contexts by means of transfer learning, whose application allows for
the addition of new sensors, the deployment into new environments and the
retraining of the model with minimum amounts of data
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