{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:05:21Z","timestamp":1785409521297,"version":"3.56.0"},"reference-count":34,"publisher":"ASME International","issue":"10","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52188102"],"award-info":[{"award-number":["52188102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Fault diagnosis is important for the complex equipment, and physics-informed fault diagnosis has become an emerging trend. While physics-informed fault diagnosis is hard to realize, unless several problems are addressed, one of the limitations is searching for the best architecture, which influences the performance greatly. Neural architecture search (NAS) has been a research hotspot. However, limited by computing resources and the deviation of supernet prediction, NAS might miss the best architecture, and impedes the application of NAS in physics-informed fault diagnosis greatly. Thus, this article proposes an evolutionary one-shot NAS method based on single-path cells (SPC-NAS) for physics-informed fault diagnosis. The proposed method develops a new supernet based on single-path cells, to reduce the computing resources and improve the reusability. An improved supernet training method is introduced to reduce the deviation between the one-shot model prediction and the stand-alone model accuracy. Finally, an evolutionary search strategy with constraint is developed to find the best architecture. The experimental results show that the proposed method can automatically find the best architecture for different tasks, which achieved an accuracy of 100% in Case Western Reserve University (CWRU) dataset with only 0.292 M parameters. All the results indicate that the proposed method can address the limitation to search the best architecture and provides a foundation for future integration with physics-informed methods.<\/jats:p>","DOI":"10.1115\/1.4071864","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:59:01Z","timestamp":1778090341000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":1,"title":["An Evolutionary One-Shot Neural Architecture Search Method Based on Single-Path Cells Toward Physics-Informed Fault Diagnosis"],"prefix":"10.1115","volume":"26","author":[{"given":"Yiping","family":"Gao","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology School of Mechanical Science and Engineering, , 1037# Luoyu Road, \u00a0 ,","place":["Wuhan, China, 430074"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Gao","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology School of Mechanical Science and Engineering, , 1037# Luoyu Road, \u00a0 ,","place":["Wuhan, China, 430074"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00p991c53","id-type":"ROR","asserted-by":"publisher"}],"name":"Huazhong University of Science and Technology School of Mechanical Science and Engineering, , 1037# Luoyu Road, \u00a0 ,","place":["Wuhan, China, 430074"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demin","family":"Yang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00p991c53","id-type":"ROR","asserted-by":"publisher"}],"name":"Huazhong University of Science and Technology School of Mechanical Science and Engineering, , 1037# Luoyu Road, \u00a0 ,","place":["Wuhan, China, 430074"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"issue":"4","key":"2026052012550072100_CIT0001","doi-asserted-by":"publisher","first-page":"041006","DOI":"10.1115\/1.4067986","article-title":"Hybrid Physics-Infused One-Dimensional Convolutional Neural Network-Based Ensemble Learning Framework for Diesel Engine Fault Diagnostics","volume":"25","author":"Singh","year":"2025","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"3","key":"2026052012550072100_CIT0002","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1109\/TII.2025.3638047","article-title":"Touch: A New Paradigm Based on Machine Tactile Sensation for Surface Microdefect Detection","volume":"22","author":"Xi","year":"2026","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"2026052012550072100_CIT0003","doi-asserted-by":"publisher","first-page":"031005","DOI":"10.1115\/1.4067600","article-title":"Data-Driven Digital Twins for Real-Time Machine Monitoring: A Case Study on a Rotating Machine","volume":"25","author":"Cheruku","year":"2025","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"2","key":"2026052012550072100_CIT0004","doi-asserted-by":"publisher","first-page":"021009","DOI":"10.1115\/1.4063401","article-title":"A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions","volume":"24","author":"Wu","year":"2024","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2026052012550072100_CIT0005","doi-asserted-by":"publisher","first-page":"103616","DOI":"10.1016\/j.inffus.2025.103616","article-title":"An Adaptive Fused Domain-Cycling Variational Generative Adversarial Network for Machine Fault Diagnosis Under Data Scarcity","volume":"126","author":"Wang","year":"2026","journal-title":"Inf. Fusion"},{"issue":"13","key":"2026052012550072100_CIT0006","doi-asserted-by":"publisher","first-page":"23002","DOI":"10.1109\/JIOT.2024.3377731","article-title":"Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM","volume":"11","author":"Keshun","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"2026052012550072100_CIT0007","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1109\/TIM.2019.2903699","article-title":"A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching","volume":"69","author":"Mao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2026052012550072100_CIT0008","doi-asserted-by":"publisher","first-page":"107331","DOI":"10.1016\/j.measurement.2019.107331","article-title":"Multiple Wavelet Regularized Deep Residual Networks for Fault Diagnosis","volume":"152","author":"Zhao","year":"2020","journal-title":"Measurement"},{"issue":"3","key":"2026052012550072100_CIT0009","doi-asserted-by":"publisher","first-page":"031002","DOI":"10.1115\/1.4054690","article-title":"Machine Health Management System Using Moving Average Feature With Bidirectional Long-Short Term Memory","volume":"23","author":"Mubarak","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2026052012550072100_CIT0010","doi-asserted-by":"publisher","first-page":"119175","DOI":"10.1016\/j.ins.2023.119175","article-title":"A Transfer Learning Strategy Based on Numerical Simulation Driving 1D Cycle-GAN for Bearing Fault Diagnosis","volume":"642","author":"Liu","year":"2023","journal-title":"Inf. Sci."},{"key":"2026052012550072100_CIT0011","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.jmapro.2024.03.028","article-title":"An Imbalanced Small Sample Slab Defect Recognition Method Based on Image Generation","volume":"118","author":"Fu","year":"2024","journal-title":"J. Manuf. Process."},{"key":"2026052012550072100_CIT0012","doi-asserted-by":"publisher","first-page":"111877","DOI":"10.1016\/j.cie.2026.111877","article-title":"Physics-Guided Neural Process With Adaptive Learning for Uncertainty Quantification of Aero-Engine Remaining Useful Life","volume":"214","author":"Tian","year":"2026","journal-title":"Comput. Ind. Eng."},{"issue":"4","key":"2026052012550072100_CIT0013","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447582","article-title":"A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions","volume":"54","author":"Ren","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"2026052012550072100_CIT0014","doi-asserted-by":"publisher","first-page":"120186","DOI":"10.1016\/j.ins.2024.120186","article-title":"Efficient Multi-Objective Neural Architecture Search Framework via Policy Gradient Algorithm","volume":"661","author":"Lyu","year":"2024","journal-title":"Inf. Sci."},{"issue":"1","key":"2026052012550072100_CIT0015","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1109\/TNNLS.2021.3096658","article-title":"RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning","volume":"34","author":"Tan","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2026052012550072100_CIT0016","doi-asserted-by":"publisher","first-page":"108914","DOI":"10.1016\/j.asoc.2022.108914","article-title":"Decomposed Neural Architecture Search for Image Denoising","volume":"124","author":"Li","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"2026052012550072100_CIT0017","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1007\/978-3-030-58517-4_32","volume-title":"Computer Vision\u2014ECCV 2020","author":"Guo","year":"2020"},{"issue":"3","key":"2026052012550072100_CIT0018","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10462-024-11058-w","article-title":"Systematic Review on Neural Architecture Search","volume":"58","author":"Salmani Pour Avval","year":"2025","journal-title":"Artif. Intell. Rev."},{"issue":"11","key":"2026052012550072100_CIT0019","doi-asserted-by":"publisher","first-page":"8138","DOI":"10.1109\/TII.2022.3177662","article-title":"A Hierarchical Training-Convolutional Neural Network for Imbalanced Fault Diagnosis in Complex Equipment","volume":"18","author":"Gao","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"2026052012550072100_CIT0020","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery With Massive Data","volume":"72\u201373","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"issue":"11","key":"2026052012550072100_CIT0021","doi-asserted-by":"publisher","first-page":"7076","DOI":"10.1109\/TIE.2016.2586442","article-title":"Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment","volume":"63","author":"Liao","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"2026052012550072100_CIT0022","doi-asserted-by":"publisher","first-page":"111950","DOI":"10.1016\/j.ymssp.2024.111950","article-title":"A Trackable Multi-Domain Collaborative Generative Adversarial Network for Rotating Machinery Fault Diagnosis","volume":"224","author":"Wang","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"2026052012550072100_CIT0023","doi-asserted-by":"publisher","first-page":"125214","DOI":"10.1016\/j.eswa.2024.125214","article-title":"MRCFN: A Multi-Sensor Residual Convolutional Fusion Network for Intelligent Fault Diagnosis of Bearings in Noisy and Small Sample Scenarios","volume":"259","author":"Ye","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2026052012550072100_CIT0024","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s00521-024-10445-2","article-title":"Scalable Reinforcement Learning-Based Neural Architecture Search","volume":"37","author":"Cassimon","year":"2025","journal-title":"Neural Comput. Appl."},{"issue":"10","key":"2026052012550072100_CIT0025","doi-asserted-by":"publisher","first-page":"19342","DOI":"10.1109\/TNNLS.2025.3588558","article-title":"NACHOS: Neural Architecture Search for Hardware-Constrained Early-Exit Neural Networks","volume":"36","author":"Gambella","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2026052012550072100_CIT0026","doi-asserted-by":"publisher","first-page":"111466","DOI":"10.1016\/j.knosys.2024.111466","article-title":"\u2113 -DARTS: Light-Weight Differentiable Architecture Search With Robustness Enhancement Strategy","volume":"288","author":"Hu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"2026052012550072100_CIT0027","doi-asserted-by":"publisher","first-page":"12219","DOI":"10.1109\/ICCV48922.2021.01202","article-title":"FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search","author":"Chu","year":"2021"},{"key":"2026052012550072100_CIT0028","doi-asserted-by":"publisher","first-page":"116027","DOI":"10.1016\/j.eswa.2021.116027","article-title":"One-Shot Neural Architecture Search for Fault Diagnosis Using Vibration Signals","volume":"190","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"2026052012550072100_CIT0029","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.isatra.2020.08.010","article-title":"Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study","volume":"107","author":"Zhao","year":"2020","journal-title":"ISA Trans."},{"key":"2026052012550072100_CIT0030","doi-asserted-by":"publisher","first-page":"110377","DOI":"10.1016\/j.measurement.2021.110377","article-title":"Finding the Optimal Multilayer Network Structure Through Reinforcement Learning in Fault Diagnosis","volume":"188","author":"Cao","year":"2022","journal-title":"Measurement"},{"key":"2026052012550072100_CIT0031","doi-asserted-by":"publisher","first-page":"107417","DOI":"10.1016\/j.measurement.2019.107417","article-title":"A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis","volume":"154","author":"Wang","year":"2020","journal-title":"Measurement"},{"key":"2026052012550072100_CIT0032","doi-asserted-by":"publisher","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"2026052012550072100_CIT0033","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2019.05.052","article-title":"An Improved Deep Convolutional Neural Network With Multi-Scale Information for Bearing Fault Diagnosis","volume":"359","author":"Huang","year":"2019","journal-title":"Neurocomputing"},{"issue":"9","key":"2026052012550072100_CIT0034","doi-asserted-by":"publisher","first-page":"5735","DOI":"10.1109\/TII.2019.2955540","article-title":"Understanding and Learning Discriminant Features Based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis","volume":"16","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/26\/10\/101008\/7611914\/jcise-25-1574.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/26\/10\/101008\/7611914\/jcise-25-1574.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:55:07Z","timestamp":1779296107000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/26\/10\/101008\/1232911\/An-Evolutionary-One-Shot-Neural-Architecture"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4071864","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]},"article-number":"101008"}}