Fault detection and diagnosis for smart buildings: State of the art, trends and challenges

Lazarova-Molnar, Sanja; Shaker, Hamid Reza; Mohamed, Nader; Jôrgensen, Bo Nørregaard

doi:10.1109/icbdsc.2016.7460392

Cited by 49 publications

(23 citation statements)

References 35 publications

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“…where,  is a constant and a is the classification accuracy.The fitness of each feature is evaluated by considering the decision variable values into thefitness function defined by the user which is represented in Eqn. (17) and the resultant values are stored as a given matrix in Eqn. (18).…”

Section: Fitness Evaluationmentioning

confidence: 99%

“…So to select the sensitive features through dimension reduction strategies feature selection is employed [16]. The selected features in the fault classification step, are used to train artificial intelligence techniques [17], and finally by these techniques a determined mechanical health conditions is attained. Now, the concerns of adapting the increasing data's volume into the value is a significant problem.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Keshari¹,

Krishna²

2019

IJITEE

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Intelligent fault detection is promising to deal with big data due to its ability in rapidly and efficiently processing collected signals and providing accurate detection results. In traditional fault detection methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise, such processes take advantage of human ingenuity but are time-consuming. Inspired by the idea of unsupervised feature learning artificial intelligence techniques are used to learn features from the raw data.As the dimensionality increases, the accuracy of fault identification methods implemented on big data decreases significantly. For supervised learning, large volume of data is needed which leads to high cost and time consuming. In this paper, an unsupervised learning approach is proposed on the basis of weighted softmax regression for fault detection using the power signals. In the proposed approach, the features are extracted from the unlabelled data. The developed approach is based on squirrel search algorithm and voting based weighted softmax regression with jaya optimization algorithm (IWSRJO_SSA). The proposed approach is simple and easy to carry out, though it attains high accuracy when compared to that of more advanced techniques. The features from the signals are extracted normally and some of the best features are selected using the squirrel search algorithm (SSA). Because of selecting the best features and using the improved weighted softmax regression, the proposed method achieves high accuracy in fault detection. Experiment on the power signal dataset shows the applicability of the proposed approach in fault detection on big data. The proposed system will be applicable on medical field, industrial field, electrical field and so on. The experimental results prove that the proposed method attains high accuracy and is superior to the other advanced methods.

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Section: Fitness Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Keshari¹,

Krishna²

2019

IJITEE

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“…As demonstrated by Lazarova-Molnar et al [8], faults in buildings can be very costly in terms of energy consumption, depending on the type of fault. Some of the most energy consuming faults identified were related to HVAC and lighting.…”

Section: Fdd In Smart Buildingsmentioning

confidence: 99%

Mobile crowdsourcing of occupant feedback in smart buildings

Lazarova-Molnar

Logason

Andersen

et al. 2017

SIGAPP Appl. Comput. Rev.

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Energy consumption of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas include rigorous measures aimed at reducing the energy consumption and, thereby, the carbon footprint. Timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) have the potential to reduce energy consumption cost by approximately 15--30%. Most FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data about faults and related events. Based on our experience, such data is very sparse and inadequate, mostly because of the difficulty and lack of incentive to collect such data in a structured manner. In this article we introduce the idea of using crowdsourcing to support FDD-related data collection, and illustrate the concept through a mobile application that has been implemented for this purpose. Furthermore, we describe our experience from using the mobile application in a university building and propose a strategy of how to successfully deploy the application in new buildings.

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“…Today the figure points towards a contribution of around 40%. In addition to this, buildings account for approximately 20% of total CO2 emissions (Lazarova-Molnar et al 2016). Thus, there is an excellent opportunity for reducing energy consumption and CO2 emissions if the general performance of energy-consuming equipment in buildings could be improved.…”

Section: Introductionmentioning

confidence: 99%

“…There are different faults in buildings. Examples are duct leakages in ventilation system, simultaneous heating/cooling, and dampers in ventilation system not working properly (Lazarova-Molnar et al 2016). Thus, there is a need to detect those faults early so their impact on energy consumption will be minimized.…”

Section: Introductionmentioning

confidence: 99%

Adverse condition and critical event prediction in commercial buildings: Danish case study

et al. 2018

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Over the last two decades, there has been a growing realization that the actual energy performances of many buildings fail to meet the original intent of building design. Faults in systems and equipment, incorrectly configured control systems and inappropriate operating procedures increase the energy consumption about 20% and therefore compromise the building energy performance. To improve the energy performance of buildings and to prevent occupant discomfort, adverse condition and critical event prediction plays an important role. The Adverse Condition and Critical Event Prediction Toolbox (ACCEPT) is a generic framework to compare and contrast methods that enable prediction of an adverse event, with low false alarm and missed detection rates. In this paper, ACCEPT is used for fault detection and prediction in a real building at the University of Southern Denmark. To make fault detection and prediction possible, machine learning methods such as Kernel Density Estimation (KDE), and Principal Component Analysis (PCA) are used. A new PCA-based method is developed for artificial fault generation. While the proposed method finds applications in different areas, it has been used primarily for analysis purposes in this work. The results are evaluated, discussed and compared with results from Canonical Variate Analysis (CVA) with KDE. The results show that ACCEPT is more powerful than CVA with KDE which is known to be one of the best multivariate data-driven techniques in particular, under dynamically changing operational conditions.

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Fault detection and diagnosis for smart buildings: State of the art, trends and challenges

Cited by 49 publications

References 35 publications

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Mobile crowdsourcing of occupant feedback in smart buildings

Adverse condition and critical event prediction in commercial buildings: Danish case study

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