Assessment of industrial fault diagnosis using rough approximations of fuzzy hypersoft sets
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| Izdano u: | PLoS One vol. 20, no. 9 (Sep 2025), p. e0329185 |
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| Glavni autor: | |
| Daljnji autori: | , , |
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Public Library of Science
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| Online pristup: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0329185 |2 doi | |
| 035 | |a 3249219150 | ||
| 045 | 2 | |b d20250901 |b d20250930 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Abdullah, Muhammad | |
| 245 | 1 | |a Assessment of industrial fault diagnosis using rough approximations of fuzzy hypersoft sets | |
| 260 | |b Public Library of Science |c Sep 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions. To address these limitations, this paper proposes a novel diagnostic framework based on Hypersoft Fuzzy Rough Set (HSFRS) theory.This hybrid approach integrates the flexibility of hypersoft sets for modeling multi-parameter relationships, the strength of fuzzy logic in handling vagueness, and the approximation capabilities of rough set theory to manage data uncertainty. Using a pseudo fuzzy binary relation, we define lower and upper approximation operators for fuzzy subsets within the parameter space. An enhanced Bingzhen and Weimin model-based decision-making algorithm is developed to support intelligent diagnosis. A case study involving a conveyor belt system is presented, evaluating eight fault states using five primary parameters and twenty sub-parameters. The results confirm the robustness, interpretability, and effectiveness of the proposed model in complex industrial scenarios by ranking the states based on fuzzy hypersoft closeness degrees. | |
| 653 | |a Fault diagnosis | ||
| 653 | |a Fuzzy sets | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Signal processing | ||
| 653 | |a Decision making | ||
| 653 | |a Approximation | ||
| 653 | |a Similarity measures | ||
| 653 | |a Set theory | ||
| 653 | |a Automation | ||
| 653 | |a Belt conveyors | ||
| 653 | |a Uncertainty | ||
| 653 | |a Parameters | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Khuram Ali Khan | |
| 700 | 1 | |a Atiqe Ur Rahman | |
| 700 | 1 | |a Mabela, Rostin Matendo | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 9 (Sep 2025), p. e0329185 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249219150/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3249219150/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249219150/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |