{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T20:57:43Z","timestamp":1784926663891,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault diagnosis methods based on deep learning have made certain progress in recent years. However, in actual industrial scenarios, there are severe strong background noise and limited computing resources, which poses challenges to the practical application of fault diagnosis models. In response to the above issues, this paper proposes a novel noise-resistant and lightweight fault diagnosis framework with nonlinear timestep degenerative greedy strategy (NTDGS) and dual residual horizontal feature pyramid (DRHFPN) for fault diagnosis in strong noise scenarios. This method takes advantage of the strong fitting ability of deep learning methods to model the agent in reinforcement learning by the ways of parameterization, fully leveraging the advantages of both deep learning and reinforcement learning methods. NTDGS is further developed to adaptively adjust the action sampling strategy of the agent at different training stages, improving the convergence speed of the network. To enhance the noise resistance of the network, DRHFPN is constructed, which can filter out interference noise at the feature map level by fusing local feature details and global semantic information. Furthermore, the feature map weighting attention mechanism (FMWAM) is designed to enhance the weak feature extraction ability of the network through adaptive weighting of the feature maps. Finally, the performance of the proposed method is evaluated in different datasets and strong noise environments. Experiments show that in various fault diagnosis scenarios, the proposed method has better noise resistance, higher fault diagnosis accuracy, and fewer parameters compared to other methods.<\/jats:p>","DOI":"10.3390\/s25247639","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:36:53Z","timestamp":1765895813000},"page":"7639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Improved DQN Framework with Dual Residual Horizontal Feature Pyramid for Autonomous Fault Diagnosis in Strong-Noise Scenarios"],"prefix":"10.3390","volume":"25","author":[{"given":"Sha","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4930-6284","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3986-4219","authenticated-orcid":false,"given":"Weiting","family":"Gan","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103287","DOI":"10.1016\/j.aei.2025.103287","article-title":"Addressing Unknown Faults Diagnosis of Transport Ship Propellers System Based on Adaptive Evolutionary Reconstruction Metric Network","volume":"65","author":"Wang","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102739","DOI":"10.1016\/j.inffus.2024.102739","article-title":"A Multi-Source Domain Feature-Decision Dual Fusion Adversarial Transfer Network for Cross-Domain Anti-Noise Mechanical Fault Diagnosis in Sustainable City","volume":"115","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7950","DOI":"10.1109\/TSMC.2025.3598790","article-title":"A Virtual Domain-Driven Semi-Supervised Hyperbolic Metric Network with Domain-Class Adversarial Decoupling for Aircraft Engine Intershaft Bearings Fault Diagnosis","volume":"55","author":"Wang","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8270","DOI":"10.1109\/TII.2025.3588608","article-title":"Continuous Evolution Learning: A Lightweight Expansion-Based Continuous Learning Method for Train Transmission Systems Fault Diagnosis","volume":"21","author":"Wang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3506412","DOI":"10.1109\/TIM.2023.3238032","article-title":"CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement","volume":"72","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116746","DOI":"10.1016\/j.jsv.2022.116746","article-title":"An Improved Envelope Spectrum via Candidate Fault Frequency Optimization-Gram for Bearing Fault Diagnosis","volume":"523","author":"Cheng","year":"2022","journal-title":"J. Sound Vib."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12377","DOI":"10.1109\/TNNLS.2024.3483954","article-title":"An Interpretable Sparse Time-Frequency Representation Method Toward Mechanical Fault Diagnosis","volume":"36","author":"Lin","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108897","DOI":"10.1016\/j.ymssp.2022.108897","article-title":"Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings","volume":"171","author":"Chen","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108082","DOI":"10.1016\/j.ress.2021.108082","article-title":"A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery","volume":"217","author":"Tang","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, F., Yang, W., Wei, K., Ball, A., Cattley, R., and Qin, B. (2024). Acoustic monitoring of wind turbine blades using wavelet packet analysis and 1D convolutional neural networks. Struct. Health Monit., 14759217241293006.","DOI":"10.1177\/14759217241293006"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108185","DOI":"10.1016\/j.measurement.2020.108185","article-title":"An optimized VMD method and its applications in bearing fault diagnosis","volume":"166","author":"Li","year":"2020","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2856","DOI":"10.1109\/TII.2022.3183548","article-title":"The methodology of modified frequency band envelope kurtosis for bearing fault diagnosis","volume":"19","author":"Hua","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108751","DOI":"10.1016\/j.ymssp.2021.108751","article-title":"Generalized gini indices: Complementary sparsity measures to box-cox sparsity measures for machine condition monitoring","volume":"169","author":"Hou","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1109\/TSP.2018.2813305","article-title":"A noise resistant correlation method for period detection of noisy signals","volume":"66","author":"Fan","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"76108","DOI":"10.1088\/1361-6501\/ad3295","article-title":"A new fault diagnosis of rolling bearing on FFT image coding and L-CNN","volume":"35","author":"Cui","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14971","DOI":"10.1109\/ACCESS.2021.3052852","article-title":"Early fault diagnosis of shaft crack based on double optimization maximum correlated kurtosis deconvolution and variational mode decomposition","volume":"9","author":"Ma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"3505009","article-title":"Active fault diagnosis for linear systems: Within a signal processing framework","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.neucom.2018.12.088","article-title":"A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals","volume":"396","author":"He","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24308","DOI":"10.1109\/JSEN.2024.3414299","article-title":"Incipient Fault Diagnosis Method via Joint Adaptive Signal Decomposition","volume":"24","author":"Wu","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7514","DOI":"10.1109\/JSEN.2023.3248285","article-title":"Multivariate dynamic mode decomposition and its application to bearing fault diagnosis","volume":"23","author":"Zhang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/TCYB.2025.3531494","article-title":"Learning to Imbalanced Open Set Generalize: A Meta-Learning Framework for Enhanced Mechanical Diagnosis","volume":"55","author":"Wang","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112134","DOI":"10.1016\/j.ymssp.2024.112134","article-title":"Surrogate Modeling of Pantograph-Catenary System Interactions","volume":"224","author":"Cheng","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109848","DOI":"10.1016\/j.ymssp.2022.109848","article-title":"Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise","volume":"186","author":"Yu","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.isatra.2023.12.045","article-title":"Anti-noise transfer adversarial convolutions with adaptive threshold for rotating machine fault diagnosis","volume":"146","author":"Wang","year":"2024","journal-title":"ISA Trans."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.isatra.2022.06.035","article-title":"Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis","volume":"133","author":"Zhang","year":"2023","journal-title":"ISA Trans."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13462","DOI":"10.1109\/TIE.2022.3144572","article-title":"Dual-path mixed-domain residual threshold networks for bearing fault diagnosis","volume":"69","author":"Chen","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110570","DOI":"10.1016\/j.ress.2024.110570","article-title":"A Novel Reinforcement Learning Agent for Rotating Machinery Fault Diagnosis with Data Augmentation","volume":"253","author":"Li","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11428","DOI":"10.1109\/TNNLS.2024.3516035","article-title":"Learn to Supervise: Deep Reinforcement Learning-Based Prototype Refinement for Few-Shot Motor Fault Diagnosis","volume":"36","author":"Xia","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105845","DOI":"10.1016\/j.conengprac.2024.105845","article-title":"A Deep Reinforcement Learning-Based Intelligent Fault Diagnosis Framework for Rolling Bearings under Imbalanced Datasets","volume":"145","author":"Li","year":"2024","journal-title":"Control Eng. Pract."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121001","DOI":"10.1016\/j.eswa.2023.121001","article-title":"An Adaptive Fault Diagnosis Framework under Class-Imbalanced Conditions Based on Contrastive Augmented Deep Reinforcement Learning","volume":"234","author":"Zhao","year":"2023","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:17:11Z","timestamp":1766121431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":30,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25247639"],"URL":"https:\/\/doi.org\/10.3390\/s25247639","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]}}}