{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T09:27:02Z","timestamp":1769938022683,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Project of Gansu Province","award":["24CXGA042"],"award-info":[{"award-number":["24CXGA042"]}]},{"name":"Scientific and Technological Project of Gansu Province","award":["62263019"],"award-info":[{"award-number":["62263019"]}]},{"name":"Scientific and Technological Project of Gansu Province","award":["22YF7FA166"],"award-info":[{"award-number":["22YF7FA166"]}]},{"name":"Scientific and Technological Project of Gansu Province","award":["22YF7GA164"],"award-info":[{"award-number":["22YF7GA164"]}]},{"name":"National Science Foundation of China","award":["24CXGA042"],"award-info":[{"award-number":["24CXGA042"]}]},{"name":"National Science Foundation of China","award":["62263019"],"award-info":[{"award-number":["62263019"]}]},{"name":"National Science Foundation of China","award":["22YF7FA166"],"award-info":[{"award-number":["22YF7FA166"]}]},{"name":"National Science Foundation of China","award":["22YF7GA164"],"award-info":[{"award-number":["22YF7GA164"]}]},{"name":"Gansu Provincial Science and Technology Program","award":["24CXGA042"],"award-info":[{"award-number":["24CXGA042"]}]},{"name":"Gansu Provincial Science and Technology Program","award":["62263019"],"award-info":[{"award-number":["62263019"]}]},{"name":"Gansu Provincial Science and Technology Program","award":["22YF7FA166"],"award-info":[{"award-number":["22YF7FA166"]}]},{"name":"Gansu Provincial Science and Technology Program","award":["22YF7GA164"],"award-info":[{"award-number":["22YF7GA164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Accurate fault diagnosis in analog circuits faces significant challenges owing to the inherent complexity of fault data patterns and the limited feature representation capabilities of conventional methodologies. Addressing the limitations of current convolutional neural networks (CNN) in handling heterogeneous fault characteristics, this study presents an efficient channel attention-enhanced multi-input CNN framework (ECA-MI-CNN) with dual-domain feature fusion, demonstrating three key innovations. First, the proposed framework addresses multi-domain feature extraction through parallel CNN branches specifically designed for processing time-domain and frequency-domain features, effectively preserving their distinct characteristic information. Second, the incorporation of an efficient channel attention (ECA) module between convolutional layers enables adaptive feature response recalibration, significantly enhancing discriminative feature learning while maintaining computational efficiency. Third, a hierarchical fusion strategy systematically integrates time-frequency domain features through concatenation and fully connected layer transformations prior to classification. Comprehensive simulation experiments conducted on Butterworth low-pass filters and two-stage quad op-amp dual second-order low-pass filters demonstrate the framework\u2019s superior diagnostic capabilities. Real-world validation on Butterworth low-pass filters further reveals substantial performance advantages over existing methods, establishing an effective solution for complex fault pattern recognition in electronic systems.<\/jats:p>","DOI":"10.3390\/computation13040094","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T11:26:41Z","timestamp":1744284401000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fault Diagnosis in Analog Circuits Using a Multi-Input Convolutional Neural Network with Feature Attention"],"prefix":"10.3390","volume":"13","author":[{"given":"Hui","family":"Yuan","sequence":"first","affiliation":[{"name":"Gansu Provincial Water Environment Monitoring Center, Lanzhou 730030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8533-3901","authenticated-orcid":false,"given":"Yaoke","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"GS-Unis Intelligent Transportation System & Control Technology Co., Ltd., Lanzhou 730050, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9637-9937","authenticated-orcid":false,"given":"Guobi","family":"Ling","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Jingxiao","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Zhiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1109\/TIE.2012.2224074","article-title":"Diagnostics and prognostics method for analog electronic circuits","volume":"60","author":"Vasan","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102323","DOI":"10.1016\/j.aei.2023.102323","article-title":"WavePHMNet: A comprehensive diagnosis and prognosis approach for analog circuits","volume":"59","author":"Khemani","year":"2024","journal-title":"Adv. Eng. Informatics"},{"key":"ref_3","first-page":"1133","article-title":"Fault diagnosis of analog circuit for WPA-IGA-BP neural network","volume":"43","author":"Wang","year":"2021","journal-title":"Syst. Eng. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"159204","DOI":"10.1007\/s11432-018-9807-7","article-title":"Fault diagnosis of industrial process based on the optimal parametric t-distributed stochastic neighbor embedding","volume":"64","author":"Jia","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_5","first-page":"434","article-title":"Analog circuit diagnostic method based on multi-kernel learning multiclass rele-vance vector machine","volume":"45","author":"Gao","year":"2019","journal-title":"Acta Autom. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chang, Y., Li, H., and You, G. (2024). Research on Building Energy Consumption Prediction Based on Improved PSO Fusion LSSVM Model. Energies, 17.","DOI":"10.3390\/en17174329"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TIM.2018.2809839","article-title":"Parallel\u2013series multiobjective genetic algorithm for optimal tests selection with multiple constraints","volume":"67","author":"Yang","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","first-page":"1","article-title":"Fault Detection Unknown Input Observer for Local Nonlinear Fuzzy Autonomous Ground Vehicles System Based on a Joint Peak-to-Peak Analysis and Zonotopic Analysis Threshold","volume":"7","author":"Li","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"020501","DOI":"10.1088\/1674-1056\/23\/2\/020501","article-title":"Studies of phase return map and symbolic dynamics in a periodically driven Hodgkin\u2013Huxley neuron","volume":"23","author":"Ding","year":"2014","journal-title":"Chin. Phys. B"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jmsy.2022.09.003","article-title":"Improved spiking neural network for intershaft bearing fault diagnosis","volume":"65","author":"Wang","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106852","DOI":"10.1016\/j.engappai.2023.106852","article-title":"Fault diagnosis of power-shift system in continuously variable transmission tractors based on improved echo state network","volume":"126","author":"Wang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/82.823545","article-title":"Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor","volume":"47","author":"Aminian","year":"2000","journal-title":"IEEE Trans. Circuits Syst. II-Analog. Digit. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TIM.2018.2836058","article-title":"RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits","volume":"68","author":"Binu","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1016\/j.neucom.2015.09.050","article-title":"Analog circuit fault diagnosis based UCISVM","volume":"173","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10489-025-06278-8","article-title":"Unsupervised feature learning using locality-preserved auto-encoder with complexity-invariant distance for intelligent fault diagnosis of machinery","volume":"55","author":"Lu","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7496","DOI":"10.1109\/TIE.2020.3003649","article-title":"Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery","volume":"68","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1007\/s12145-024-01257-y","article-title":"Design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for remote sensing lithological mapping using calculated dropout","volume":"17","author":"Otele","year":"2024","journal-title":"Earth Sci. Informatics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107474","DOI":"10.1016\/j.patcog.2020.107474","article-title":"A novel hybrid approach for crack detection","volume":"107","author":"Fang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.measurement.2010.10.004","article-title":"A novel approach of analog circuit fault diagnosis using support vector machines classifier","volume":"44","author":"Cui","year":"2011","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TASL.2011.2109382","article-title":"Acoustic modeling using deep belief networks","volume":"20","author":"Mohamed","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neucom.2018.05.024","article-title":"An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition","volume":"310","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral-spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Su, X., Cao, C., Zeng, X., Feng, Z., Shen, J., Yan, X., and Wu, Z. (2021). Application of DBN and GWO-SVM in analog circuit fault diagnosis. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-86916-6"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1145\/2001269.2001295","article-title":"Unsupervised learning of hierarchical representations with convolutional deep belief networks","volume":"54","author":"Lee","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2023.3243853","article-title":"Single-source domain expansion network for cross-scene hyperspectral image classification","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6640","DOI":"10.1109\/TIM.2020.2969008","article-title":"Generative adversarial networks with comprehensive wavelet feature for fault diagnosis of analog circuits","volume":"69","author":"He","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103109","DOI":"10.1016\/j.dsp.2021.103109","article-title":"Target exaggeration for deep learning-based speech enhancement","volume":"116","author":"Kim","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1049\/el.2019.2892","article-title":"Analogue circuit fault diagnosis based on convolution neural network","volume":"55","author":"Du","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zha, D., Wang, L., and Mu, N. (2021). A novel analog circuit soft fault diagnosis method based on convolutional neural network and backward difference. Symmetry, 13.","DOI":"10.3390\/sym13061096"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/LCOMM.2020.2970397","article-title":"PRI modulation recognition based on squeeze-and-excitation networks","volume":"24","author":"Wei","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"69838","DOI":"10.1109\/ACCESS.2020.2983436","article-title":"Multi-scale weighted fusion attentive generative adversarial network for single image de-raining","volume":"8","author":"Bi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Com-Putation"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S.Q., and Sun, J. (2016, January 27). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TNNLS.2019.2920905","article-title":"Person reidentification via multi-feature fusion with adaptive graph learning","volume":"31","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10032-019-00348-7","article-title":"MA-CRNN: A multi-scale attention CRNN for Chinese text line recognition in natural scenes","volume":"23","author":"Tong","year":"2019","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tang, H., Gao, S., Wang, L., Li, X., Li, B., and Pang, S. (2021). A novel intelligent fault diagnosis method for rolling bearings based on wasserstein generative adversarial network and convolutional neural network under unbalanced dataset. Sensors, 21.","DOI":"10.3390\/s21206754"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/4\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:11Z","timestamp":1760029991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/4\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computation13040094"],"URL":"https:\/\/doi.org\/10.3390\/computation13040094","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]}}}