{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:35:43Z","timestamp":1770741343329,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Youth Fund of Shandong Natural Science Foundation","award":["ZR2022QF084"],"award-info":[{"award-number":["ZR2022QF084"]}]},{"name":"The Youth Fund of Shandong Natural Science Foundation","award":["ZR2021MF042"],"award-info":[{"award-number":["ZR2021MF042"]}]},{"name":"The Youth Fund of Shandong Natural Science Foundation","award":["2022KJ234"],"award-info":[{"award-number":["2022KJ234"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2022QF084"],"award-info":[{"award-number":["ZR2022QF084"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2021MF042"],"award-info":[{"award-number":["ZR2021MF042"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["2022KJ234"],"award-info":[{"award-number":["2022KJ234"]}]},{"name":"The Youth Innovation Team Development Program of Shandong Provincial Higher Education Institutions","award":["ZR2022QF084"],"award-info":[{"award-number":["ZR2022QF084"]}]},{"name":"The Youth Innovation Team Development Program of Shandong Provincial Higher Education Institutions","award":["ZR2021MF042"],"award-info":[{"award-number":["ZR2021MF042"]}]},{"name":"The Youth Innovation Team Development Program of Shandong Provincial Higher Education Institutions","award":["2022KJ234"],"award-info":[{"award-number":["2022KJ234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen\u2013Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.<\/jats:p>","DOI":"10.3390\/sym16060720","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T10:45:56Z","timestamp":1718016356000},"page":"720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Xinjia","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Siting","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Wenmin","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Control and Electronic Technology, Beijing 100080, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4794-0107","authenticated-orcid":false,"given":"Yunlong","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Xuye","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0844-4418","authenticated-orcid":false,"given":"Jiancheng","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1109\/TIE.2018.2835373","article-title":"Robust Circuit Parameters Design for the CLLC-Type DC Transformer in the Hybrid AC\u2013DC Microgrid","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TIM.2017.2775438","article-title":"Method for Local Parametric Fault Diagnosis of a Broad Class of Analog Integrated Circuits","volume":"67","author":"Tadeusiewicz","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, N. (2020). The analysis of electronic circuit fault diagnosis based on neural network data fusion algorithm. Symmetry, 12.","DOI":"10.3390\/sym12030458"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"229","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":"2021","journal-title":"Sci. China Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Marin, C.V., Constantinescu, F., and Nitescu, M. (2011). A dictionary approach to fault diagnosis of analog circuits. IEEE Africon\u201911, IEEE.","DOI":"10.1109\/AFRCON.2011.6072155"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1080\/15325008.2013.830659","article-title":"Synchronous generator off-line diagnosis approach including fault detection and estimation of failures on machine parameters","volume":"41","author":"Lalami","year":"2013","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_8","unstructured":"Contu, S., Fanni, A., Marchesi, M., Montisci, A., and Serri, A. (1996, January 16). Wavelet analysis for diagnostic problems. Proceedings of the 8th Mediterranean Electrotechnical Conference, Bari, Italy."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hong, S., Tang, J., and Chen, X. (2010, January 5\u20137). Analog circuit fault diagnosis combing wavelet packet with higher order statistics. Proceedings of the 2010 2nd International Conference on Signal Processing Systems, Dalian, China.","DOI":"10.1109\/ICSPS.2010.5555644"},{"key":"ref_10","first-page":"151","article-title":"A neural network approach for fault diagnosis of large-scale analogue circuits","volume":"4","author":"He","year":"2002","journal-title":"IEEE Int. Symp. Circuits Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20210174","DOI":"10.1587\/elex.18.20210174","article-title":"Incipient fault diagnosis of analog circuits based on wavelet transform and improved deep convolutional neural network","volume":"18","author":"Yang","year":"2021","journal-title":"IEICE Electron. Express"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, G., Feng, D., and Tang, W. (2022). Electrical impedance tomography based on grey wolf optimized radial basis function neural network. Micromachines, 13.","DOI":"10.3390\/mi13071120"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.4028\/www.scientific.net\/AMM.427-429.1048","article-title":"WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit","volume":"427\u2013429","author":"Gan","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s10470-018-1362-7","article-title":"A novel approach for fault detection of analog circuit by using improved EEMD","volume":"98","author":"Shokrolahi","year":"2019","journal-title":"Analog. Integrated. Circuits Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7657054","DOI":"10.1155\/2016\/7657054","article-title":"Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM","volume":"2016","author":"Xiong","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18305","DOI":"10.1109\/ACCESS.2020.2968744","article-title":"Data-driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1109\/TSP.2013.2265222","article-title":"Empirical Wavelet Transform","volume":"61","author":"Gilles","year":"2013","journal-title":"Signal Process. IEEE Trans."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bi, X., Cao, S., and Zhang, D. (2019). Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum. Energies, 12.","DOI":"10.3390\/en12040661"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23053","DOI":"10.1109\/ACCESS.2018.2823765","article-title":"Analog Circuit Incipient Fault Diagnosis Method Using DBN Based Features Extraction","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"92517","DOI":"10.1109\/ACCESS.2019.2923242","article-title":"Research on ELM soft fault diagnosis of analog circuit based on KSLPP feature extraction","volume":"7","author":"Gan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1007\/s00034-021-01842-2","article-title":"An Analog Circuit Fault Diagnosis Approach Based on Improved Wavelet Transform and MKELM","volume":"1","author":"Zhang","year":"2022","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1742-6596\/1871\/1\/012053","article-title":"Kent-PSO optimized ELM fault diagnosis model in analog circuits","volume":"1871","author":"Liu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10836-016-5597-x","article-title":"The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM","volume":"32","author":"Yu","year":"2016","journal-title":"J. Electron. Test."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18107","DOI":"10.1007\/s11042-021-10602-y","article-title":"Spam review detection using self attention based CNN and bi-directional LSTM","volume":"80","author":"Bhuvaneshwari","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.measurement.2018.02.044","article-title":"A novel approach for analog circuit fault diagnosis based on Deep Belief Network","volume":"121","author":"Zhao","year":"2018","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1007\/s00034-020-01595-4","article-title":"A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks","volume":"40","author":"Gao","year":"2020","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liang, H., Zhu, Y., Zhang, D., Chang, L., Lu, Y., Zhao, X., and Guo, Y. (2021). Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection. Electronics, 10.","DOI":"10.3390\/electronics10121496"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"137945","DOI":"10.1109\/ACCESS.2019.2943071","article-title":"Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine","volume":"7","author":"Yuan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2986852","article-title":"A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features","volume":"70","author":"Gao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","first-page":"183","article-title":"Research on analog circuit fault diagnosis method","volume":"40","author":"Lin","year":"2017","journal-title":"Mod. Electron. Tech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3560","DOI":"10.1109\/TSP.2011.2143711","article-title":"Wavelet Transform With Tunable Q-Factor","volume":"59","author":"Selesnick","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105018","DOI":"10.1088\/1361-6501\/abf25e","article-title":"Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform","volume":"32","author":"Hu","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119643","DOI":"10.1016\/j.eswa.2023.119643","article-title":"Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method","volume":"219","author":"Pramanik","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, G., Guan, K., Yang, J., Zou, L., and Yang, X. (2023). Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network. Electronics, 12.","DOI":"10.3390\/electronics12244910"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Abdelwanis, M.I., El-Sousy, F.F.M., and Ali, M.M. (2023). A Fuzzy-Based Proportional\u2013Integral\u2013Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor. Electronics, 12.","DOI":"10.3390\/electronics12244955"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, J., Ma, C., Zhang, Y., Huang, H., Kong, D., and Ni, W. (2023). Multi-Label Diagnosis of Arrhythmias Based on a Modified Two-Category Cross-Entropy Loss Function. Electronics, 12.","DOI":"10.3390\/electronics12244976"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11233","DOI":"10.1002\/int.23040","article-title":"A particle swarm algorithm optimization-based SVM\u2013KNN algorithm for epileptic EEG recognition","volume":"37","author":"Wang","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yuan, X., Sheng, Y., Zhuang, X., Yin, J., and Yang, S. (2024). A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0291660"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10470-016-0721-5","article-title":"Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits","volume":"87","author":"Song","year":"2016","journal-title":"Analog. Integr. Circ. Sig Process"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s10836-016-5616-y","article-title":"A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO","volume":"32","author":"Zhang","year":"2016","journal-title":"J. Electron. Test."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/6\/720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:56:20Z","timestamp":1760108180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/6\/720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,10]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["sym16060720"],"URL":"https:\/\/doi.org\/10.3390\/sym16060720","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,10]]}}}