Application of PCA for early leak detection in a pipeline system of a steam boiler

Swiercz, Miroslaw; Mroczkowska, Halina

doi:10.15199/48.2019.10.43

Cited by 2 publications

(3 citation statements)

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“…In most cases, the reaction of the solution presented above preceded the moment when the fault symptoms were noticed by the personnel. Number of cases, for which the leakage was predicted 1 2 7 8 5 n days before its detection by the personnel In our opinion, the proposed MPCA approach, or the approach based on a standard PCA model studied in [32], can replace the necessity of the construction and identification of the process model, which is time consuming and relatively expensive, so for those reasons not applicable in Elektrocieplownia Bialystok. The method described in the paper can effectively extract information of growing leakages, so the implementation of our detection approach may be the important element of an on-line system, which would lead to significant improvement in safety and maintenance of industrial boilers in Elektrocieplownia Bialystok.…”

Section: Discussionmentioning

confidence: 99%

“…The inspections performed during repair confirmed that the common boiler fault was the pipeline leak in the riser and downcomer sections, mostly due to aging and thermal stress. The faults of this kind are rather difficult to detect at their early stage by the diagnostic systems or by the process operator during usual on-line monitoring of the plant [32].…”

Section: Boiler Water System In Elektrocieplownia Bialystokmentioning

confidence: 99%

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Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Swiercz

Mroczkowska²

2020

Sensors

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In the paper the usability of the Multiway PCA (MPCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented. A long segment of measurements of selected process variables was divided into a series of “batches” (representing daily recordings of normal behavior of the plant) and used to create the MPCA model of a “healthy” system in a reduced space of three principal components (PC). The periodically updated MPCA model was used to establish the confidence ellipsoid for the “healthy” system in the PC coordinates. The staff’s decision of the probable leak detection is supported by comparison of the current location of the operating point (on the “fault trajectory”) with the boundaries of the confidence ellipsoid. It must be emphasized that due to daily and seasonal changes of heat/electricity demands, the process variables have substantially greater variability than in the examples of batch processes studied in literature. Despite those real challenges for the MPCA method, numerical examples confirmed that the presented approach was able to foresee the leaks earlier than the operator, typically 3–5 days before the boiler shutdown. The presented methodology may be useful in implementation of an on-line system, developed to improve safety and maintenance of boilers in a thermal-electrical power plant.

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Section: Discussionmentioning

confidence: 99%

Section: Boiler Water System In Elektrocieplownia Bialystokmentioning

confidence: 99%

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Swiercz

Mroczkowska²

2020

Sensors

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“…For instance, PCA has been utilized for early fault detection and classification (Li and Wen 2014 ; Cotrufo and Zmeureanu 2016 ; Chen and Wen 2017 ; Swiercz and Mroczkowska 2019 ), IEQ (Mansor et al. 2021 ), energy consumption prediction (Sha et al.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

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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Application of PCA for early leak detection in a pipeline system of a steam boiler

Cited by 2 publications

References 37 publications

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

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

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