A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance

Yang, Chunsheng; Shen, Weiming; Qiangqiang, Chen; Gunay, H. Burak

doi:10.1016/j.jobe.2017.10.013

Cited by 60 publications

(24 citation statements)

References 20 publications

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“…Furthermore, 46 rows are excluded from the training process because of missing critical details such as claim textual description and the root cause input. Moreover, scales (8)(9)(10) in severity and (7-10) in occurrence had an insufficient number of claims (lower than 50 rows) for every element, these records are excluded too, as shown in Table 2a. The reason behind that, AutoML platform cannot start training with less than 50 readings per class.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…FMEA was firstly developed by NASA in 1963 to enhance the performance of the devices that are used in the aerospace industry [8]. Later, FMEA was adopted and promoted by Ford Motors in 1977 [3].…”

Section: Introductionmentioning

confidence: 99%

“…The RPN parameters' values are determined by engineers and experts which may include uncertainty and vagueness [12]. Moreover, the parameters that are used in FMEA are represented by (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) crisp scale which is an unreliable representation of real-application cases [5,13]. Additionally, Chang, et al in [3] have criticized the RPN estimation by the inhomogeneous morphologic correlations between the three parameters.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

2020

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In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques.

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Section: Data Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

2020

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“…Therefore, the reduction of building energy consumption is crucial in order to achieve the goals in terms of decarbonization recently established in Paris Agreement. To this scope, several actions are required, such as: building envelope refurbishments [140][141][142], optimization of the HVAC systems [143][144][145][146][147][148][149], utilization of renewable energy sources [150][151][152][153][154][155][156]. This topic was initially marginally investigated during the first SDEWES conferences.…”

Section: Building Efficiencymentioning

confidence: 99%

Recent Advances in the Analysis of Sustainable Energy Systems

et al. 2018

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EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency.

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“…Although the conventional RPN method has been proven to be a useful tool in real applications, 8,9 it still has several shortcomings: [10][11][12] (1) it is sometimes difficult for FMEA team members to give precise evaluations of risk factors. (2) The mathematical equation for computing RPN is debatable. (3) The weights of the three risk factors are not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis‐technique for order preference by similarity to ideal solution method

Nie

Liu

et al. 2019

Quality & Reliability Eng

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Failure mode and effects analysis (FMEA) is a prospective risk assessment tool used to identify, assess, and eliminate potential failure modes (FMs) in various industries to improve security and reliability. However, the traditional FMEA method has been criticized for several shortcomings and even the improved FMEA methods based on predefined linguistic terms cannot meet the needs of FMEA team members' diversified opinion expressions. To solve these problems, a novel FMEA method is proposed by integrating Bayesian fuzzy assessment number (BFAN) and extended gray relational analysis-technique for order preference by similarity to ideal solution (GRA-TOPSIS) method.First, the BFANs are used to flexibly describe the risk evaluation results of the identified failure modes. Second, the Hausdorff distance between BFANs is calculated by using the probability density function (PDF). Finally, on the basis of the distance, the extended GRA-TOPSIS method is applied to prioritize failure modes. A simulation study is presented to verify the effectiveness of the proposed approach in dealing with vague concepts and show its advantages over existing FMEA methods. Furthermore, a real case concerning the risk evaluation of aero-engine turbine and compressor blades is provided to illustrate the practical application of the proposed method and particularly show the potential of using the BFANs in capturing FMEA team members' diverse opinions. KEYWORDSBayesian fuzzy assessment number, failure mode and effects analysis, GRA-TOPSIS, Hausdorff distance, probability density function

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A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance

Cited by 60 publications

References 20 publications

Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

Recent Advances in the Analysis of Sustainable Energy Systems

Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis‐technique for order preference by similarity to ideal solution method

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