Failure mode and effects analysis based on intuitionistic fuzzy sets and evidential correlation coefficient

Zhang, Hang; Chan, Huang; Lu, Mang; Dong, Xiaofei

doi:10.1109/icus48101.2019.8995990

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…Table 7 shows the team setup, where three operators op 1 , op 2 , and op 3 constitute the expert panel (N P = 3). The weight panel w P can be calculated using the equations ( 20), (21):…”

Section: Resultsmentioning

confidence: 99%

“…Knowledge inaccuracies can be attributed to slightly detailed information or the existence of new unknown events. Knowledge uncertainty has been represented using fuzzy systems [17], evidence theory [18] [19] [20], or hybrid systems [21] [22]. The next step in consolidating acquired knowledge is to define a strategy for knowledge uncertainty quantification.…”

Section: A Extraction Of Tacit Knowledge and Uncertainty Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

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Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the process and reduce stopovers because it can provide constant valuable information regarding the machine condition to the operators. However, formalizing operational (tacit) knowledge to explicit knowledge is not an easy task. This transformation considers modeling expert knowledge, quantification of knowledge uncertainty, and validation of the acquired knowledge. This study proposes a novel approach for production assessment using a knowledge transfer framework and evidence theory to address the aforementioned challenges. The main contribution of this paper is a methodology for the formalization of tacit knowledge based on an extended failure mode and effect analysis for knowledge extraction, as well as the use of evidence theory for the uncertainty definition of knowledge. Moreover, this approach uses primitive recursive functions for knowledge modeling and proposes a validation strategy of the knowledge using machine data. These elements are integrated into an interactive recommendation system hosted on a backend that uses HoloLens as a visual interface. We demonstrate this approach using an industrial setup: a laboratory bulk good system. The results yield interesting insights, including the knowledge validation, uncertainty behavior of knowledge, and interactive troubleshooting for the machine operator.

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“…Table 7 shows the team setup, where three operators op 1 , op 2 , and op 3 constitute the expert panel (N P = 3). The weight panel w P can be calculated using the equations ( 20), (21):…”

Section: Resultsmentioning

confidence: 99%