Artificial intelligence assisted false alarm detection and diagnosis system development for reducing maintenance cost of chillers at the data centre

Lee, Dasheng; Lai, Chih-Wei; Liao, Kuo-Kai; Chang, Jia‐Wei

doi:10.1016/j.jobe.2020.102110

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…For example, a large data center operator used predictive maintenance to monitor the condition of its cooling towers and identify potential issues early. By addressing these issues proactively, the operator was able to reduce downtime and improve overall efficiency (Jain, Pistikopoulos & Mannan, 2019, Lee, et. al., 2021, Zhang, et.…”

Section: Predictive Maintenance In Data Center Cooling Towersmentioning

confidence: 99%

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Wisdom Ebirim,

Favour Oluwadamilare Usman,

Kehinde Andrew Olu-lawal

et al. 2024

World J. Adv. Res. Rev.

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Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

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Section: Predictive Maintenance In Data Center Cooling Towersmentioning

confidence: 99%

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Wisdom Ebirim,

Favour Oluwadamilare Usman,

Kehinde Andrew Olu-lawal

et al. 2024

World J. Adv. Res. Rev.

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“…In response to the aforementioned challenge, a novel AIassisted system for false alarm detection and diagnosis, referred to as AI-FADD, was proposed. This system not only achieved a noteworthy reduction in the false alarm rejection rate but also resulted in substantial cost savings in terms of labor [106]. The utilization of Automated Fault Detection and Diagnosis (AFDD), leveraging operational data from Air Handling Units (AHUs), effectively mitigated energy wastage and enhanced occupant comfort.…”

Section: Ai For Fault Detection and Diagnosticsmentioning

confidence: 99%

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Hanafi,

Moawed,

Abdellatif

2024

Engineering Research Journal (Shoubra)

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This paper explores artificial intelligence's (AI) transformative potential in optimizing energy management within buildings, aligning with environmental objectives and sustainable practices. AI-based methodologies are pivotal in identifying inefficiencies, forecasting future energy requirements, and mitigating energy wastage. Adopting AI-driven energy management systems enhances efficiency, reduces costs, and contributes to a decreased building environmental footprint. Furthermore, AI empowers buildings to actively participate in energy markets by accurately predicting real-time supply and demand without operational disruption. The study delves into various AI applications, including energy prediction, optimization, fault detection and diagnosis (FDD), and real-world implementations. Notably, AI's role in fault detection and diagnostics is highlighted, emphasizing its substantial contribution to diagnostic precision. Specific numerical outcomes from reviewed studies underscore the tangible impact of AI techniques. Predictive control powered by AI achieved a remarkable 20% reduction in heating energy without compromising comfort. Additionally, smart home energy management algorithms demonstrated a notable 22.63% decrease in electricity costs and a 22.77% reduction in the peak-to-average ratio. These concrete figures underscore the practical success of AI techniques in significantly reducing energy consumption. This review affirms the transformative potential of AI in building energy management. Including specific numerical values from empirical studies adds a quantitative dimension to the discussion, providing clear evidence of the positive impact of AI on energy efficiency.

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“…Lee et al developed several supervised clustering methods to detect false alarm warnings of chillers in a data center [49]. The authors found their multiclass neural network to have the best performance, with a 99.6% prediction accuracy on its testing dataset.…”

Section: Supervised Learningmentioning

confidence: 99%

“…In addition, generalization of machine learning analysis is an ongoing problem [49,75,104]. Research has been completed over many specific buildings or pre-compiled datasets, but little research has been completed which has been designed to apply across a variety of buildings with different configurations [111].…”

Section: Challengesmentioning

confidence: 99%

Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review

Nelson

Culp

2022

Energies

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Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.

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Artificial intelligence assisted false alarm detection and diagnosis system development for reducing maintenance cost of chillers at the data centre

Cited by 15 publications

References 25 publications

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review

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