Using Expert Knowledge to Generate Data for Broadband Line Prognostics Under Limited Failure Data Availability

Purpose The purpose of this paper is to investigate the perceptions of key informants on a national support programme for the development of new care models (NCM) in England (2015/2016–2017/2018). It focuses on the perceived facilitators and barriers affecting the development and implementation of the NCM programme and offers some insight into the role of national level support in enabling local integration initiatives. Design/methodology/approach A set of 29 interviews were carried out with a variety of respondents at the national level (including current and past programme leads, strategic account managers, advisors to the programme and external regulators) between October 2017 and March 2018, and analysed thematically. Findings A set of facilitative elements of the programme were identified: the development of relationships and alliances, strong local and national leadership, the availability of expert knowledge and skills, and additional funding. Challenges to success included perceived expectations from the national Vanguard programme, oversight and performance monitoring, engagement with regulators, data availability and quality, as well as timetables and timescales. Crucially, the facilitators and challenges were found to interact in dynamic and complex ways, which resulted in significant tensions and ambiguities within the support programme. Research limitations/implications While the sample was drawn from a range of different senior players and the authors ensured a diverse sample associated with the NCM support programme, it inevitably cannot be complete and there may have been valuable perspectives absent. Originality/value The paper demonstrates that the analysis of facilitators and challenges with respect to the national support of implementation of integrated care initiatives should move beyond the focus on separate influencing factors and address the tensions that the complex interplay among these factors create.

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Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x17742765 ◽

2018 ◽

Vol 232 (5) ◽

pp. 505-523 ◽

Cited By ~ 1

Author(s):

Haoyuan Zhang ◽

D William R Marsh

Keyword(s):

Bayesian Network ◽

Expert Knowledge ◽

Network Models ◽

Rail Network ◽

Periodic Inspection ◽

Failure Data ◽

Typical Data ◽

Maintenance Decisions ◽

Maintenance Decision

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.

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2017 Frank Newman Speller Award: Knowledge-Based Predictive Analytics in Corrosion

CORROSION ◽

10.5006/2635 ◽

2017 ◽

Vol 74 (2) ◽

pp. 181-196 ◽

Cited By ~ 3

Author(s):

Narasi Sridhar

Keyword(s):

Expert Knowledge ◽

Predictive Analytics ◽

Low Frequency ◽

Conceptual Approach ◽

Correlative Analysis ◽

Knowledge Based ◽

Failure Data ◽

Analysis Theory ◽

Analytic Strategy ◽

Semi Empirical

Corrosion researchers have developed many approaches to predicting the occurrence of different corrosion modes. Four types of predictive analytics can be identified: data-centric correlative analysis, theory-based semi-empirical models, expert-knowledge-based models, and theory-based, multi-scale models. However, most new corrosion failures have been serendipitous discoveries, rather than anticipated through a systematic process. This paper reviews stress corrosion cracking (SCC) of carbon steel in non-aqueous electrolytes and in aqueous solutions of oxyanions, to understand whether using the appropriate predictive analytic strategy may have helped anticipate the failures. In all of these cases of SCC, some information was available in related environments prior to field failures, but a framework was lacking to identify the connections and anticipate failures. Data-centric predictive analytics would not have helped anticipate the failures because of the low frequency of the phenomena and the lack of prior failure data. A better predictive analytic strategy will need methods to integrate diverse sources of knowledge into a theoretical framework. Predictive analytics also must have a probabilistic component because both the knowledge and data are uncertain. The paper provides a conceptual approach to developing such a predictive analytics framework.

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Artificial Intelligence in Health Informatics: Hype or Reality?

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677951 ◽

2019 ◽

Vol 28 (01) ◽

pp. 003-004 ◽

Cited By ~ 1

Author(s):

Kate Fultz Hollis ◽

Lina F. Soualmia ◽

Brigitte Séroussi

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Expert Knowledge ◽

Improve Patient Care ◽

Health Data ◽

Machine Learning Algorithms ◽

Data Availability ◽

Data Driven ◽

Editorial Team ◽

Special Topic

Objectives: To provide an introduction to the 2019 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The special topic is discussed, the IMIA President’s statement is introduced, and changes in the Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose in the 1970’s from new approaches for representing expert knowledge with computers. Since then, AI in medicine has gradually evolved toward essentially data-driven approaches with great results in image analysis. However, data integration, storage, and management still present clear challenges among which the lack of explanability of the results produced by data-driven AI methods. Conclusion: With more health data availability, and the recent developments of efficient and improved machine learning algorithms, there is a renewed interest for AI in medicine.The objective is to help health professionals improve patient care while also reduce costs. However, the other costs of AI, including ethical issues when processing personal health data by algorithms, should be included.

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Failure Assessment of Corrosion Affected Pipeline Networks with Limited Failure Data Availability

Process Safety and Environmental Protection ◽

10.1016/j.psep.2021.11.024 ◽

2021 ◽

Author(s):

Vahid Aryai ◽

Hassan Baji ◽

Mojtaba Mahmoodian

Keyword(s):

Data Availability ◽

Failure Data ◽

Failure Assessment ◽

Pipeline Networks

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A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil

Sustainability ◽

10.3390/su132011267 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11267

Author(s):

Afshin Ghahramani ◽

John McLean Bennett ◽

Aram Ali ◽

Kathryn Reardon-Smith ◽

Glenn Dale ◽

...

Keyword(s):

Soil Erosion ◽

Expert Knowledge ◽

Model Performance ◽

Ground Cover ◽

Bayesian Belief Network ◽

Data Availability ◽

Network Modelling ◽

Practical Result ◽

Belief Network ◽

Active Coal

Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.

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