A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis.
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| Title: | A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis. |
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| Authors: | Hayat B; School of Information Engineering, Xi'an Eurasia University, Xi'an, Shaanxi, China., Ahmad S; School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China., Shahid MA; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China., Khan A; School of Information Engineering, Xi'an Eurasia University, Xi'an, Shaanxi, China., Islam MR; Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh., Sayeed MS; Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia., Ullah Y; Centre for Wireless Technology, Faculty of AI and Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia. |
| Source: | PloS one [PLoS One] 2026 Jun 02; Vol. 21 (6), pp. e0349385. Date of Electronic Publication: 2026 Jun 02 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0349385 |
