Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Nikpour, Hoda; Aamodt, Agnar

doi:10.1007/s13748-020-00227-x

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The processing of heterogeneous information is a key point in the decision-making process [47,48]. As case events are usually characterized by risk, complexity and uncertainty [49], plus the imprecision of the environment, decision information is often not always expressed as accurate numbers, including Boolean values, interval numbers and fuzzy numbers. In addition, because of the fuzziness of human thinking, it is sometimes difficult to express the decision information with quantitative values in the decision-making process, and qualitative language information is also used to evaluate the attributes [50].…”

Section: Local Similarity Measurement Methods For Case Informationmentioning

confidence: 99%

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

et al. 2022

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Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy.The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

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Section: Local Similarity Measurement Methods For Case Informationmentioning

confidence: 99%

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

et al. 2022

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“…Nikpour et al [18] use Bayesian posterior distributions to modify or add features to input case descriptions to increase accuracy of similarity assessments in case retrieval. They also use the same approach to provide explanations for case failures in different domains [17]. This approach is similar to BCBR, but BCBR constructs new features which are also added to the case base-cases rather than modifying input cases.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Flogard

Mengshoel

Bach

2021

Case-Based Reasoning Research and Development

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Checklists are used to aid the fulfillment of safety critical activities in a variety of different applications, such as aviation, health care or labour inspections. However, optimizing a checklist for a specific purpose can be challenging. Checklists also need to be trustworthy and user friendly to promote user compliance. With labour inspections as a starting point, we introduce the Checklist Construction Problem. To address the problem, we seek to optimize the content of labour inspection checklists in order to improve the working conditions in every organisation targeted for inspections. To do so, we introduce a hybrid framework called BCBR to construct trustworthy checklists. BCBR is based on case-based reasoning (CBR) and Bayesian inference (BI) and constructs new checklists based on past cases. A key novelty of BCBR is the use of BI for constructing new features in past cases. The augmented past cases are retrieved via CBR to construct new checklists, which ensures justification for the content of the checklists and promotes trust. Experiments suggest that BCBR is more effective than any other baseline we tested, in terms of constructing trustworthy checklists.

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“…Fortunately, as another important AI technique representing the knowledge in form of a case, case-based reasoning (CBR) is an empirical and knowledge-based reasoning method that draws on human thinking to deal with uncertain problems and is becoming widely popular for implementing the intelligence in various engineering areas. , CBR can make use of its idea of selecting similar (even the same) cases to cope with the frequently changing working conditions and provide targeted solutions. In view of the excellent combined performance of CBR and BN, as well as its sound application effects, the integration of BN and CBR has attracted increasing attentions and interests from lots of experts and scholars in various areas. − Nevertheless, none of the existing literature considers the integration of BN and CBR to study the operational adjustment for the PQC problem of pharmaceutical manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

et al. 2023

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In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.

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Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Cited by 5 publications

References 21 publications

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

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