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Автор: Grafiati
Опубліковано: 6 вересня 2023

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1

James, Ajith Tom, O. P. Gandhi, and S. G. Deshmukh. "Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience." Journal of Advances in Management Research 14, no. 4 (October 2, 2017): 425–45. http://dx.doi.org/10.1108/jamr-02-2017-0024.

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Purpose The purpose of this paper is to develop an ontological model of failure knowledge of automobile systems that will enhance the knowledge management of automobile system failures, which will help for design and maintenance of automobiles. Failure knowledge of automobile systems and components gained through maintenance and repair can mitigate future failures, if integrated in the design. This is an outcome of this paper. Design/methodology/approach A failure coding scheme is developed for assimilating various entities of automobile failure knowledge and an ontological model is developed for its systematic structuring and representation. The developed failure code is a combination of alphanumeric and numeric code that incorporates ingredients of the failure knowledge, which will help database management, with reduced data entry time and storage space. Findings The maintenance of automobiles not only brings back the systems into operating conditions but also convey a lot of information regarding the failures. This is a useful input to the designers in development of reliable and maintainable automobile systems. A knowledge base can be created for automobile systems/components failures from their maintenance and service experience. Research limitations/implications Developed ontological model of automobile failure knowledge gained through maintenance experience can be shared across automobile manufacturers and service providers. This would help in design improvements, with ease and efficient undertaking of maintenance activities. This paper proposes the conceptual ontology structure, which is populated with three cases of automobile maintenance. Originality/value This research work is a first attempt to develop an ontological model for automobile failures from their maintenance and service experience. The novelty of the work is in its explicit consideration of all knowledge related to failures and maintenance of automobile systems, with their coding and structuring.
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2

NAKAO, Masayuki. "To Use Failure Knowledge." Journal of the Society of Mechanical Engineers 106, no. 1010 (2003): 52–56. http://dx.doi.org/10.1299/jsmemag.106.1010_52.

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3

NAKAO, Masayuki. "Reuse of Failure Knowledge." Journal of the Society of Mechanical Engineers 107, no. 1025 (2004): 286–89. http://dx.doi.org/10.1299/jsmemag.107.1025_286.

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4

Burch, Druin. "Heart Failure: Gaps in Knowledge and Failures in Treatment." PLoS Medicine 11, no. 8 (August 12, 2014): e1001702. http://dx.doi.org/10.1371/journal.pmed.1001702.

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5

Chhetri, Tek Raj, Anelia Kurteva, Jubril Gbolahan Adigun, and Anna Fensel. "Knowledge Graph Based Hard Drive Failure Prediction." Sensors 22, no. 3 (January 27, 2022): 985. http://dx.doi.org/10.3390/s22030985.

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Анотація:
The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.
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6

Brahmbhatt, Darshan H., and Jeremy Kobulnik. "Knowledge Translation in Heart Failure." Canadian Journal of General Internal Medicine 15, SP1 (April 6, 2020): 27–35. http://dx.doi.org/10.22374/cjgim.v15isp1.421.

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The treatment of heart failure has expanded over the past three decades with large numbers of new medications and healthcare innovations available for these patients. At the same time, the prevalence of heart failure continues to increase across Canada and these patients are managed by a variety of clinicians from differing backgrounds in both primary care and hospital settings. Despite national recommendations advocating uptake of these new therapies, their utilization remains limited and inconsistent across the country. This review discusses the importance of knowledge translation in heart failure and examines the barriers to implementation of new therapies and models of care, providing a range of solutions to facilitate the delivery of guideline-directed care for heart failure patients.
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7

May, Peter J. "Policy Learning and Failure." Journal of Public Policy 12, no. 4 (October 1992): 331–54. http://dx.doi.org/10.1017/s0143814x00005602.

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ABSTRACTMultiple knowledges are available for utilisation in policy choice. The rank ordering of knowledges for use in decisionmaking is thus a fundamental predecision. This article shows how this predecision necessarily constrains the processes associated with a politics of ideas, using cases from American international commodity policy. Even when the supposed preconditions of this sort of politics are present, policy change did not occur when the proposed ideas arose from a knowledge accorded secondary status in policymaking circles. Several implications are discussed for the influence and the study of ideational politics. Ultimately, the politics of ideas, so often portrayed through cases of innovation, may be quite conservative, contained by knowledge hierarchies which reflect prior politicaxl circumstances.
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8

Meguro, Junichi, Eijiro Takeuchi, and Taro Suzuki. "Knowledge of GNSS Failure for Robotics." Journal of the Robotics Society of Japan 37, no. 7 (2019): 585–92. http://dx.doi.org/10.7210/jrsj.37.585.

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9

Roussel, Maureen G. "Improving Nurses’ Knowledge of Heart Failure." Journal for Nurses in Professional Development 31, no. 4 (2015): 211–17. http://dx.doi.org/10.1097/nnd.0000000000000164.

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10

IINO, Kenji. "Managing Failure Knowledge with the Computer." Journal of the Society of Mechanical Engineers 106, no. 1016 (2003): 537–40. http://dx.doi.org/10.1299/jsmemag.106.1016_537.

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11

NAKAO, Masayuki. "Organized Activity for Failure Knowledge Management." Journal of the Society of Mechanical Engineers 108, no. 1036 (2005): 213–16. http://dx.doi.org/10.1299/jsmemag.108.1036_213.

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12

Storey, John, and Elizabeth Barnett. "Knowledge management initiatives: learning from failure." Journal of Knowledge Management 4, no. 2 (June 2000): 145–56. http://dx.doi.org/10.1108/13673270010372279.

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13

Dracup, Kathleen, Debra K. Moser, Michele M. Pelter, Thomas Nesbitt, Jeffrey Southard, Steven M. Paul, Susan Robinson, Jessica Zègre Hemsey, and Lawton Cooper. "Rural Patients’ Knowledge About Heart Failure." Journal of Cardiovascular Nursing 29, no. 5 (2014): 423–28. http://dx.doi.org/10.1097/jcn.0b013e31829cbcf3.

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14

KAWAMURA, Masaya, and Michio OBARA. "Construction of "Failure and Knowledge Database"." Proceedings of the 1992 Annual Meeting of JSME/MMD 2000 (2000): 633–34. http://dx.doi.org/10.1299/jsmezairiki.2000.0_633.

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15

Lee, Michelle L., та Brad Sherrod. "Improving patientsʼ knowledge about kidney failure". Nursing Critical Care 10, № 1 (січень 2015): 41–48. http://dx.doi.org/10.1097/01.ccn.0000457310.26630.71.

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16

Strömberg, Anna. "Measuring Patients' Knowledge about Heart Failure." European Journal of Cardiovascular Nursing 4, no. 4 (December 2005): 267–68. http://dx.doi.org/10.1016/j.ejcnurse.2005.10.007.

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17

Ali, Nazakat, and Jang-Eui Hong. "Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base." Computers 7, no. 4 (December 6, 2018): 68. http://dx.doi.org/10.3390/computers7040068.

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Анотація:
Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.
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18

Nomali, Mahin, Kian Alipasandi, and Ramin Mohammadrezaei. "Knowledge regarding Heart Failure: A Reflection on Current Disease Knowledge State among Iranian Patients with Heart Failure." Journal of Medical Research 5, no. 4 (September 5, 2019): 155–58. http://dx.doi.org/10.31254/jmr.2019.5405.

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Анотація:
Background: Heart failure (HF) is a serious problem with an increasing prevalence globally. Low level of HF knowledge may cause low compliance and low quality of life and, poor self-care. On the other hand, assessing the level of HF knowledge is necessary in order to apply educational programs. Aims and objectives: the aim was to determine knowledge regarding HF among Iranian patients with HF. Study Design: This was a cross sectional study. Setting: We conducted this study at the HF clinic of Tehran Heart Center (THC) affiliated with Tehran University of Medical Sciences (TUMS, Tehran, Iran). Materials and Methods: In this cross-sectional study, 190 patients older than 18 years old, with confirmed diagnosis of HF for at least 3 months by an HF specialist, NYHA function class II to IV and an ability of reading and writing Farsi language were included during June 2017 and January 2018 by consecutive sampling. Data were gathered in a short form, including demographic and clinical variables. Knowledge regarding HF was measured by the Dutch HF knowledge scale (Cronbach’s alpha=0.62) with 15- multiple choice item. The score range varied between 0 (no knowledge) and 15 (optimum knowledge). Scores were reported totally and in 3 areas of knowledge. Scores higher than the median was considered as higher knowledge. Statistics: The SPSS software version 16 was used to describe data. Normality of continuous variables was checked by the Kolmogorov-Smirnoff test. Frequency and partial frequency distribution were used to describe Categorical variables. While, we used mean, median, standard deviation, and IQR for describing continuous variables. Results: From June 2017 to January 2018, 160/190 patients with median age (IQR) of 59 (16) years old participated in the study (response rate of 84.2%). 67.5% of study patients were male (83.5%). 87.5% of then were married. The majority of patients were with NYHA function class II (60.6%), and with an etiology of ischemic heart disease (65.0%). The median and IQR of total, general, HF treatment, and symptoms/ symptom recognition knowledge were 8 (7-10), 12 (9-14), 2 (1.25-3), and 4 (3-5), respectively. Low level of total, general, HF treatment, and symptoms and symptom recognition knowledge among Iranian patients with HF were 55%, 60%, 58.8%, and 71.9%, respectively. Conclusion: Patients with HF had low levels of total, general, HF treatment, and symptoms/symptom recognition knowledge. Thus, there is an essential need to be improved by an appropriate intervention, especially on knowledge of symptoms /symptom recognition.
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19

심형석 and 홍선관. "A Study of the Trait of Leadership Failure Using Failure Knowledge." Knowledge Management Society of Korea 15, no. 3 (September 2014): 1–12. http://dx.doi.org/10.15813/kmr.2014.15.3.001.

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20

IINO, Kenji. "Making Use of Past Failure Knowledge with Virtual Failure Experience Software." Journal of the Society of Mechanical Engineers 107, no. 1031 (2004): 829–32. http://dx.doi.org/10.1299/jsmemag.107.1031_829.

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21

Holland, John. "Banks, knowledge and crisis: a case of knowledge and learning failure." Journal of Financial Regulation and Compliance 18, no. 2 (May 11, 2010): 87–105. http://dx.doi.org/10.1108/13581981011033961.

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22

Wang, Jian Ming, and Jing De Huang. "Research of Electronic Failure Knowledge Management Mechanism Based on Simulation Acquisition." Applied Mechanics and Materials 241-244 (December 2012): 396–400. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.396.

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The establishment of electronic equipment and more operating conditions of knowledge acquisition simulation environment, considering the failure of electronic equipment failure caused by the internal laws and mechanisms, to the essence of the phenomenon of electronic failure analysis, combined with the physical prototype test results and raw statistical data, research the actual process of using electronic equipment, considering the state of properties, condition monitoring parameters and the integration of traditional knowledge, expertise and reasoning techniques to be applied to physical prototype of a certain conclusion, not only for the early prediction of potential failures, laid the theoretical foundation for forecasting , and fault prediction system for the electronic equipment, the development provides important data and technical support to solve the electronic equipment maintenance and support tasks needed problem.
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23

Lin, Chin-Yen, Kathleen Dracup, Martha Biddle, and Debra Moser. "Heart Failure Symptom Knowledge Improves Heart Failure Self-Care in Rural Patients with Heart Failure." Journal of Cardiac Failure 22, no. 8 (August 2016): S6. http://dx.doi.org/10.1016/j.cardfail.2016.06.028.

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24

KOYAMA, Kei. "New release: Failure Knowledge Database (English version)." Journal of Information Processing and Management 49, no. 7 (2006): 397–401. http://dx.doi.org/10.1241/johokanri.49.397.

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25

Willette, Elizabeth W., Danielle Surrells, Leslie L. Davis, and Charles T. Bush. "Nurses' Knowledge of Heart Failure Self-Management." Progress in Cardiovascular Nursing 22, no. 4 (September 2007): 190–95. http://dx.doi.org/10.1111/j.0889-7204.2007.06403.x.

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26

Albert, Nancy M., Susan Collier, Veronica Sumodi, Sandra Wilkinson, Jeffrey P. Hammel, Linda Vopat, Cindy Willis, and Barb Bittel. "Nurses's knowledge of heart failure education principles." Heart & Lung 31, no. 2 (March 2002): 102–12. http://dx.doi.org/10.1067/mhl.2002.122837.

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27

Panda, S., and D. Mishra. "Development of a tribological failure knowledge model." International Journal of Knowledge Engineering and Data Mining 1, no. 2 (2010): 99. http://dx.doi.org/10.1504/ijkedm.2010.034839.

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28

Hathaway, Patsy. "Failure to Thrive: Knowledge for Social Workers." Health & Social Work 14, no. 2 (May 1989): 122–26. http://dx.doi.org/10.1093/hsw/14.2.122.

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29

Rabiee Rad, Mehrdad, Ghazal Ghasempour Dabaghi, and Shakiba Hassanzadeh. "Congestive kidney failure; recent knowledge and new trends." Journal of Renal Endocrinology 7, no. 1 (August 26, 2021): e17-e17. http://dx.doi.org/10.34172/jre.2021.17.

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Анотація:
One of the possible causes of renal failure due to heart failure is congestive kidney failure (CKF). Venous congestion has a key role in the development of renal dysfunction and damage in patients with heart failure. This condition is named congestive nephropathy. In this mini-review the common causes of congestive kidney failure, its pathophysiology, histopathological changes and treatment are discussed.
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30

A, Hazari. "Neurosyphilis Presenting as Failure to Thrive." Journal of Infectious Diseases & Travel Medicine 5, no. 2 (2021): 1–2. http://dx.doi.org/10.23880/jidtm-16000150.

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Neurosyphilis occurs when the nervous system is affected by the Treponema Pallidum bacterial species. Medical literature describes two clinical stages: early and late. Early can be asymptomatic or show signs of meningitis and meningoencephalitis. Conversely, late presents as parenchymal, vascular abnormalities, tabes dorsalis or syphilitic gumma. Although neurosyphilis is now uncommon, knowledge of key physical exam findings is crucial to its consideration and subsequent diagnosis and management.
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31

Sharma, Veena. "Knowledge and Expressed Practices of Dietary Regulation among Chronic Renal Failure Patients Undergoing Haemodialysis." International Journal of Nursing & Midwifery Research 06, no. 2&3 (November 13, 2019): 69–74. http://dx.doi.org/10.24321/2455.9318.201923.

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32

Hossayni, Hicham, Imran Khan, Mohammad Aazam, Amin Taleghani-Isfahani, and Noel Crespi. "SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs." Applied Sciences 10, no. 18 (September 11, 2020): 6325. http://dx.doi.org/10.3390/app10186325.

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The recent focus on sustainability and improved efficiency requires innovative approaches in industrial automation. We present SemKoRe, a knowledge graph developed to improve machine maintenance in the industrial domain. SemKoRe is vendor-agnostic, it helps Original Equipment Manufacturers (OEMs) to capture, share and exploit the failure knowledge generated by their customers machines located around the world. Based on our interactions with actual customers, it usually takes several hours to days to fix a machine-related issue. During this time, production stops and incurs cost in terms of lost production. SemKoRe significantly enhances the maintenance process by reducing the failure diagnostic time, and by centralizing machine maintenance knowledge fed by the experts and technicians around the world. We developed flexible architecture to cover our customers’ varying needs, along with failure and machine domain ontologies. To demonstrate the feasibility of SemKoRe, a proof-of-concept is developed. SemKoRe gathers all failure related data in the knowledge graph, and shares it among all connected customers in order to easily solve future failures of the same type. SemKoRe received the approval of several substantial clients located in USA, UK, France, Germany, Italy and China, associated with various segments such as pharmaceutical, automotive, HVAC and food and beverage.
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33

Cândea, Gabriela Simona, Claudiu Vasile Kifor, and Ciprian Cândea. "Knowledge Repository for Fmea Related Knowledge." ACTA Universitatis Cibiniensis 64, no. 1 (November 1, 2014): 10–16. http://dx.doi.org/10.2478/aucts-2014-0003.

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Abstract This paper presents innovative usage of knowledge system into Failure Mode and Effects Analysis (FMEA) process using the ontology to represent the knowledge. Knowledge system is built to serve multi-projects work that nowadays are in place in any manufacturing or services provider, and knowledge must be retained and reused at the company level and not only at project level. The system is following the FMEA methodology and the validation of the concept is compliant with the automotive industry standards published by Automotive Industry Action Group, and not only. Collaboration is assured trough web-based GUI that supports multiple users access at any time
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34

Rehman, Zobia, and Claudiu Vasile Kifor. "An Ontology to Support Semantic Management of FMEA Knowledge." International Journal of Computers Communications & Control 11, no. 4 (July 3, 2016): 507. http://dx.doi.org/10.15837/ijccc.2016.4.1674.

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<p>Risk mitigation has always been a special concern for organization’s strategic management. Various tools and techniques have been developed to manage risk in an effective way. Failure Mode and Effects Analysis (FMEA) is one of the tools used for effective assessment of risk. It analyzes all potential failure modes, their causes, and effects on a product or process. Moreover it recommends actions to mitigate failures in order to enhance product reliability. Organizations spend their resources and domain experts make their efforts to complete this analysis. It further helps organizations identify the expected risks and plan strategies in advance to tackle them. But unfortunately the analysis produced after spending a lot of organizational assets and experts’ struggles, is not reusable due to its natural language text based description. Information and communication technology experts proposed some solutions but they are associated with some deficiencies. Authors in [13] proposed an ontology based solution to extract and reuse FMEA knowledge from the textual documents, and this article is the first step towards its implementation. In this article we proposed our ontology for Process Failure Mode and Effects Analysis (PFMEA) for automotive domain, along with its implementation, reasoning, and data retrieval through it.</p>
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35

Wang, Mei Qing, and Bin Wang. "Key Elemental Function Identification Based on Function-Failure Knowledge." Applied Mechanics and Materials 58-60 (June 2011): 1435–41. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1435.

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Functional modeling is the key step in product design process. To improve the quality of functional modeling, an identification method of key elemental function based on function-failure knowledge was proposed. A function-failure knowledge model for mechanical product was built, which consists of function-failure mode, relationships among failure modes, and failure risk information. By means of the function-failure repository, the approach to calculate risk number (RN) of the elemental function was presented, in which the relationships among failure modes were considered. Then the key elemental functions were identified by the RN value. Finally, a case was studied to illustrate the proposed method.
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36

Al-Ahmad, Walid. "Knowledge of IT Project Success and Failure Factors." International Journal of Information Technology Project Management 3, no. 4 (October 2012): 56–71. http://dx.doi.org/10.4018/jitpm.2012100104.

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Traditionally, project success/failure is considered only after the project is completed or cancelled. Integrating project success and failure factors knowledge and software engineering activities would result in a situation where project success/failure is considered as part of the development process, leading to more successful software projects. This article aims to identify the common issues responsible for IT projects’ success/failure to develop a deeper understanding of these root causes. Knowledge about success can be used to understand failure and vice versa. Therefore, generic taxonomies of the root causes are developed for that purpose. Knowledge of these taxonomies is integrated into software development and management activities to help software developers and project managers complete projects successfully.
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37

Koji, Yusuke, Yoshinobu Kitamura, Yoshikiyo Kato, Yoshio Tsutsui, and Riichiro Mizoguchi. "Interoperability-oriented Integration of Failure Knowledge into Functional Knowledge and Knowledge Transformation based on Concepts Mapping." Transactions of the Japanese Society for Artificial Intelligence 22 (2007): 78–92. http://dx.doi.org/10.1527/tjsai.22.78.

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38

Zelenak, Christine, Sara Radenovic, Lindy Musial-Bright, Elvis Tahirovic, Mesud Sacirovic, Chong-Bin Lee, Diana Jahandar-Lashki, et al. "Heart failure awareness survey in Germany: general knowledge on heart failure remains poor." ESC Heart Failure 4, no. 3 (March 22, 2017): 224–31. http://dx.doi.org/10.1002/ehf2.12144.

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39

심형석. "An Exploratory Research for the Conceptualization Failure knowledge." Knowledge Management Society of Korea 12, no. 4 (December 2011): 121–32. http://dx.doi.org/10.15813/kmr.2011.12.4.007.

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40

Becker, George, and Eva Etzioni-Halevy. "The Knowledge Elite and the Failure of Prophecy." Social Forces 66, no. 2 (December 1987): 560. http://dx.doi.org/10.2307/2578758.

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41

Suisman, David. "Sound, Knowledge, and the “Immanence of Human Failure”." Social Text 28, no. 1 (2010): 13–34. http://dx.doi.org/10.1215/01642472-2009-058.

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42

Rodríguez-Luis, Julio. "Andrés Hurtado's Failure: Graftingmodernismointo the “Tree of Knowledge”." Romance Quarterly 35, no. 2 (May 1988): 193–204. http://dx.doi.org/10.1080/08831157.1988.9925823.

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43

Morvarid, Mahmoud. "Reference Failure, Illusion of Thought and Self-Knowledge." Dialectica 67, no. 3 (September 2013): 303–23. http://dx.doi.org/10.1111/1746-8361.12032.

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44

Hatamura, Y., K. Ilno, K. Tsuchlya, and T. Hamaguchi. "Structure of Failure Knowledge Database and Case Expression." CIRP Annals 52, no. 1 (2003): 97–100. http://dx.doi.org/10.1016/s0007-8506(07)60540-x.

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45

Gustafsson, Robin, and Erkko Autio. "A failure trichotomy in knowledge exploration and exploitation." Research Policy 40, no. 6 (July 2011): 819–31. http://dx.doi.org/10.1016/j.respol.2011.03.007.

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46

VANEMPEL, V., and L. DEWINDT. "Human heart failure: our current STATus of knowledge." Cardiovascular Research 57, no. 2 (February 1, 2003): 294–97. http://dx.doi.org/10.1016/s0008-6363(02)00825-8.

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47

PARTANEN, L. "738 Nurses' knowledge of advising heart failure patients." European Journal of Heart Failure Supplements 5, no. 1 (2006): 173. http://dx.doi.org/10.1016/s1567-4215(06)80489-3.

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48

GOMES, PAULO, CARLOS BENTO, and PEDRO GAGO. "Learning to verify design solutions from failure knowledge." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, no. 2 (April 1998): 107–15. http://dx.doi.org/10.1017/s0890060498122035.

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Анотація:
Learning is an intrinsic product of case-based reasoning. Acquiring new cases is one possible way of learning in a case-based system. These cases comprise mainly success knowledge. The successful cases are essentially used to generate new design solutions. But a case-based system also can make use of failure knowledge. In this paper we present how a case-based system can acquire failure cases for verification of the solution created by success cases. We describe IM-RECIDE, a system that uses case-based reasoning for solving design problems that are imperfectly described and explained. The learning aspect is focused and some of the machine learning dimensions in design are criticized. Experimental results in the domain of room configuration also are presented.
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49

Basch, Corey H., Charles E. Basch, Patricia Zybert, and Randi L. Wolf. "Failure of Colonoscopy Knowledge to Predict Colonoscopy Uptake." Journal of Community Health 41, no. 5 (April 20, 2016): 1094–99. http://dx.doi.org/10.1007/s10900-016-0194-6.

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50

Graham-Jones, P. J., and B. G. Mellor. "Expert and knowledge-based systems in failure analysis." Engineering Failure Analysis 2, no. 2 (June 1995): 137–49. http://dx.doi.org/10.1016/1350-6307(95)00009-f.

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