Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Zhang, Liang; Leach, Matt; Bae, Yeonjin; Cui, Borui; Bhattacharya, Saptarshi; Lee, Seungjae; Im, Piljae; Adetola, Veronica; Vrabie, Draguna; Kuruganti, Teja

doi:10.1016/j.adapen.2021.100055

Cited by 32 publications

(6 citation statements)

References 87 publications

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“…This is a difficult task, and it becomes considerably more difficult when applied to the context of building systems because the symptoms of faults and the repercussions of those faults are typically not well understood or measured. Recent research has shown an increased interest in the utilization of 'virtual sensors,' which provide estimations of values that are not directly quantifiable (Ferretti et al, 2015;Mattera et al, 2018;Nehasil et al, 2021;Zhang et al, 2021;Chen J. et al, 2022;Zhang and Leach, 2022). Even though it can be challenging or expensive to measure specific parameters and values directly, indirect measurements and analytical approaches have made it possible to develop tools that are more effective in terms of their cost-effectiveness in estimating degradation signals and current conditions.…”

Section: Recent Researchmentioning

confidence: 99%

A review of the Digital Twin technology for fault detection in buildings

Hosamo

Nielsen

Alnmr

et al. 2022

Front. Built Environ.

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This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology is one of the most intriguing newly discovered technologies rapidly evolving; however, some problems still need to be addressed. First, using Digital Twins to detect building faults to prevent future failures and cutting overall costs by improving building maintenance is still ambiguous. Second, how Digital Twin technology may be applied to discover inefficiencies inside the building to optimize energy usage is not well defined. To address these issues, we reviewed 326 documents related to Digital Twin, BIM, and fault detection in civil engineering. Then out of the 326 documents, we reviewed 115 documents related to Digital Twin for fault detection in detail. This study used a qualitative assessment to uncover Digital Twin technology’s full fault detection capabilities. Our research concludes that Digital Twins need more development in areas such as scanner hardware and software, detection and prediction algorithms, modeling, and twinning programs before they will be convincing enough for fault detection and prediction. In addition, more building owners, architects, and engineers need substantial financial incentives to invest in condition monitoring before many of the strategies discussed in the reviewed papers will be used in the construction industry. For future investigation, more research needs to be devoted to exploring how machine learning may be integrated with other Digital Twin components to develop new fault detection methods.

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Section: Recent Researchmentioning

confidence: 99%

A review of the Digital Twin technology for fault detection in buildings

Hosamo

Nielsen

Alnmr

et al. 2022

Front. Built Environ.

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“…In addition, different reviews have been introduced to discuss the advances made in this research topic, such as (Zhang et al. 2021 ; Molina-Solana et al. 2017 ; Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

“…To that end, we present in this paper a comprehensive systematic survey reflecting the latest developments in the field of AI-big data analytics and their utilization in BAMSs from different perspectives. For example, Zhang et al ( 2021 ) discuss sensor impact verification and evaluation for FDAD in energy systems, while Molina et al ( 2017 ) review the contributions of data science for building energy management issues. Moving on, data mining strategies used for building energy management are overviewed in Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…A common point among all these works is the use of the fixed-time approach. The following works are reviews on fault detection and diagnosis using data-driven modeling in various fields of engineering: photovoltaic system [37], power transformer [38], HVAC (heating, ventilation and air conditioning) system [39], building energy system [40], marine current turbine [41], gear system [42], thermal system [43], industrial system [44] and high-speed trains in intelligent transportation [45]. The present work made use of PCA, as it is the most applied technique for fault detection in the TEP benchmark.…”

Section: Introductionmentioning

confidence: 99%

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

et al. 2022

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Data acquisition in process industries usually takes place at each sampling. The disadvantage is that a considerable amount of data without new information about the state of the process is continuously transmitted and processed. This negatively affects the communication system and computational power, which is more critical nowadays given the number of variables measured, even in seconds. One solution concerns the event-driven paradigm, in which only relevant data according to a pre-defined criterion is forwarded for further processing. This work investigated the event-based threshold and delta methods in the context of fault detection. The data transmission rate was also analyzed. The well-know Tennessee Eastman problem (TEP) was used as a case study. The fault detection system was based on PCA (principal component analysis), which is widely used for this purpose in this benchmark. The results were compared with the commonly used time-based approach, for a fixed false alarm rate. The threshold rule provided similar results, but with much less data. For the delta rule, significant MDR (missed detection rate) gains of up to 74% were obtained for five of the six hard-to-detect faults, and of up to 69%, for two of the three very hard-to-detect faults. MDR values very close to zero were reached for two of the three intermediate detection faults and two of the hard-to-detect faults. The detection time was also evaluated. In this regard, considerably lower values were obtained for all intermediate detection faults, three of the hard-to-detect faults and all very hard-to-detect faults. In short, the delta method was able to improve fault detection performance, especially for hard-to-detect faults, with a considerably lower data transmission rate, around 20% on average. Event-driven data acquisition can be very attractive for process industries.

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Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Cited by 32 publications

References 87 publications

A review of the Digital Twin technology for fault detection in buildings

A review of the Digital Twin technology for fault detection in buildings

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

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

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