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Conventional models can accurately diagnose faults under a single operating condition. However, when conditions change, these models may fail to recognize new fault features, resulting in a decrease in diagnosis accuracy. To address this challenge, this paper proposes a lifelong learning-enabled fractional order-convolutional encoder model for open-circuit fault diagnosis of power converters under multi-conditions. Firstly, the model automatically extracts and identifies fault signal features using the convolutional module and the encoder module, respectively. Subsequently, the model\u2019s iterative computational process is optimized by learning historical gradient information through fractional order, and enhancing the model\u2019s ability to capture the long-term dependencies inherent in fault signals. Finally, a multilevel lifelong learning framework has been established to enable the model to continuously learn the fault features of power converter under multi-conditions, thereby avoiding catastrophic forgetting that can occur when the model learns different tasks. The proposed model effectively addresses the challenge of low fault diagnosis accuracy that occurs when the operating conditions of the power converter change, achieving a diagnosis accuracy of 96.89% across 85 fault categories under multi-conditions.<\/jats:p>","DOI":"10.3390\/s25061884","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T10:43:51Z","timestamp":1742294631000},"page":"1884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lifelong Learning-Enabled Fractional Order-Convolutional Encoder Model for Open-Circuit Fault Diagnosis of Power Converters Under Multi-Conditions"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-3969","authenticated-orcid":false,"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"},{"name":"Zhuzhou Times New Material Technology Co., Ltd., Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2395-7031","authenticated-orcid":false,"given":"Enyu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9053-5620","authenticated-orcid":false,"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhuzhou Times New Material Technology Co., Ltd., Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6026","DOI":"10.1109\/TPEL.2021.3131293","article-title":"Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review","volume":"37","author":"Moradzadeh","year":"2022","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5341","DOI":"10.1016\/j.egyr.2022.03.178","article-title":"A state-of-the-art review on wind power converter fault diagnosis","volume":"8","author":"Liang","year":"2022","journal-title":"Energy Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TIA.2022.3141034","article-title":"Separation and Validation of Bond-Wire and Solder Layer Failure Modes in IGBT Modules","volume":"58","author":"Li","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12059","DOI":"10.1109\/TPEL.2023.3283286","article-title":"Review of Fault Diagnosis and Fault-Tolerant Control Methods of the Modular Multilevel Converter Under Submodule Failure","volume":"38","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_5","first-page":"3517512","article-title":"Open-Circuit Fault Diagnosis in Power Inverters Through Currents Analysis in Time Domain","volume":"70","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6837","DOI":"10.1109\/TIA.2023.3310491","article-title":"Open-Circuit Fault Diagnosis for Three-Level ANPC Inverter Based on Predictive Current Vector Residual","volume":"59","author":"Li","year":"2023","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5267","DOI":"10.1109\/TPEL.2019.2946692","article-title":"A Fast Diagnosis Method for Both IGBT Faults and Current Sensor Faults in Grid-Tied Three-Phase Inverters With Two Current Sensors","volume":"35","author":"Li","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/TEC.2023.3263160","article-title":"An Open-Circuit Fault Detection Method of PMSM Fed by Dual Inverter With High Robustness","volume":"38","author":"Jiang","year":"2023","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1109\/JESTPE.2022.3194150","article-title":"A Simple Open-Circuit Fault Diagnosis Method for Grid-Tied T-Type Three-Level Inverters With Various Power Factors Based on Instantaneous Current Distortion","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE J. Emerg. Sel. Top. Power Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/JESTPE.2023.3345410","article-title":"Fault Diagnosis of Full-Bridge Power Converter for SRMs Based on Modified Current Detection","volume":"12","author":"Hu","year":"2024","journal-title":"IEEE J. Emerg. Sel. Top. Power Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/TPEL.2018.2814615","article-title":"PWM-VSI Fault Diagnosis for a PMSM Drive Based on the Fuzzy Logic Approach","volume":"34","author":"Yan","year":"2019","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10669","DOI":"10.1109\/TIE.2022.3222663","article-title":"An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network","volume":"70","author":"Xing","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105866","DOI":"10.1016\/j.engappai.2023.105866","article-title":"Open-circuit fault diagnosis in voltage source inverter for motor drive by using deep neural network","volume":"120","author":"Yan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3834","DOI":"10.1109\/TPEL.2022.3218427","article-title":"A Unified Open-Circuit-Fault Diagnosis Method for Three-Level Neutral-Point-Clamped Power Converters","volume":"38","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TCSI.2024.3421655","article-title":"Robust Open-Switch Fault Diagnosis of Bidirectional DC\/DC Converters Based on Extended Kalman Filter With Multiple Corrections","volume":"71","author":"Ding","year":"2024","journal-title":"IEEE Trans. Circuits Syst. I-Regul. Pap."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10183","DOI":"10.1109\/TPEL.2023.3262758","article-title":"A Comprehensive Method for Online Switch Fault Diagnosis and Capacitor Condition Monitoring of Three-Level T-Type Inverters","volume":"38","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3526915","DOI":"10.1109\/TIM.2023.3305653","article-title":"A Novel Adaptive SMO-Based Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Incipient Faults of Inverters in PMSM Drives for Electric Vehicles","volume":"72","author":"Xu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9311","DOI":"10.1109\/TPEL.2023.3270030","article-title":"A Fast Diagnosis Strategy for Inverter Open-Circuit Faults Based on the Current Path of Brushless DC Motors","volume":"38","author":"Li","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10695","DOI":"10.1109\/TPEL.2021.3061448","article-title":"Current-Based Open-Circuit Fault Diagnosis for PMSM Drives With Model Predictive Control","volume":"36","author":"Huang","year":"2021","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8008","DOI":"10.1109\/TPEL.2018.2883989","article-title":"Submodule Voltage Similarity-Based Open-Circuit Fault Diagnosis for Modular Multilevel Converters","volume":"34","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TIE.2020.2965430","article-title":"Active Common-Mode Voltage-Based Open-Switch Fault Diagnosis of Inverters in IM-Drive Systems","volume":"68","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5392","DOI":"10.1109\/TPEL.2022.3233313","article-title":"Open-Circuit Fault Diagnosis and Tolerant Control for 2\/3-Level DAB Converters","volume":"38","author":"Song","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114851","DOI":"10.1016\/j.apenergy.2020.114851","article-title":"A novel fault diagnostic method in power converters for wind power generation system","volume":"266","author":"Liang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9817","DOI":"10.1109\/TIE.2018.2880719","article-title":"An Intelligent Time-Adaptive Data-Driven Method for Sensor Fault Diagnosis in Induction Motor Drive System","volume":"66","author":"Gou","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13281","DOI":"10.1109\/TPEL.2020.2994351","article-title":"An Online Data-Driven Method for Simultaneous Diagnosis of IGBT and Current Sensor Fault of Three-Phase PWM Inverter in Induction Motor Drives","volume":"35","author":"Gou","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.egyr.2020.11.273","article-title":"Open-circuit fault diagnosis of NPC inverter IGBT based on independent component analysis and neural network","volume":"6","author":"Hu","year":"2020","journal-title":"Energy Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107481","DOI":"10.1016\/j.compeleceng.2021.107481","article-title":"OC fault diagnosis of multilevel inverter using SVM technique and detection algorithm","volume":"96","author":"Sarita","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TII.2023.3271358","article-title":"A Multi-Information Fusion Algorithm to Fault Diagnosis of Power Converter in Wind Power Generation Systems","volume":"20","author":"Liang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116213","DOI":"10.1016\/j.measurement.2024.116213","article-title":"An online open circuit faults diagnosis method for converter using the lightweight two-channel deep network","volume":"243","author":"Zhao","year":"2024","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115323","DOI":"10.1016\/j.measurement.2024.115323","article-title":"Bayesian optimization-attention-feedforward neural network based train traction motor-gearbox coupled noise prediction","volume":"238","author":"Li","year":"2024","journal-title":"Measurement"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TPEL.2022.3194113","article-title":"Open-Switch Fault Diagnosis of Three-Phase PWM Converter Systems for Magnet Power Supply on EAST","volume":"38","author":"Deng","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106370","DOI":"10.1016\/j.epsr.2020.106370","article-title":"Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression","volume":"185","author":"Kou","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7504","DOI":"10.1109\/TCYB.2020.3041850","article-title":"Short-Time Wavelet Entropy Integrating Improved LSTM for Fault Diagnosis of Modular Multilevel Converter","volume":"52","author":"Han","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_34","first-page":"3512813","article-title":"Fault Diagnosis for Power Converters Based on Incremental Learning","volume":"72","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3512416","DOI":"10.1109\/TIM.2022.3169545","article-title":"Fault Diagnosis Based on Attention Collaborative LSTM Networks for NPC Three-Level Inverters","volume":"71","author":"Si","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, T., Wu, X., Luo, Z., Chen, Y., He, C., Ding, R., Zhang, C., and Yang, J. (2024). A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network. Fractal Fract., 8.","DOI":"10.3390\/fractalfract8030134"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119398","DOI":"10.1016\/j.renene.2023.119398","article-title":"Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain","volume":"219","author":"Xu","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, S.-Y., Tam, L.-M., Wu, S.-P., Tsai, W.-L., Hu, C.-W., Cheng, L.-Y., Xu, Y.-X., and Cheng, S.-C. (2023). The Performance Investigation of Smart Diagnosis for Bearings Using Mixed Chaotic Features with Fractional Order. Sensors, 23.","DOI":"10.3390\/s23083801"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, S.S., and Guo, W. (2023). Auto-Encoders in Deep Learning-A Review with New Perspectives. Mathematics, 11.","DOI":"10.3390\/math11081777"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.neucom.2022.06.066","article-title":"Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network","volume":"501","author":"Zhong","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5362","DOI":"10.1109\/TPAMI.2024.3367329","article-title":"A Comprehensive Survey of Continual Learning: Theory, Method and Application","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7099","DOI":"10.1109\/TII.2022.3209672","article-title":"Multilevel Attention-Based Sample Correlations for Knowledge Distillation","volume":"19","author":"Gou","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127422","DOI":"10.1016\/j.neucom.2024.127422","article-title":"Online continual learning through unsupervised mutual information maximization","volume":"578","author":"Hihn","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_45","first-page":"1655","article-title":"Revisiting Natural Gradient for Deep Networks","volume":"37","author":"Pascanu","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7982","DOI":"10.1007\/s10489-024-05603-x","article-title":"A lightweight CNN-transformer model for learning traveling salesman problems","volume":"54","author":"Jung","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xie, J.L., Shi, W.F., and Shi, Y.Q. (2022). Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM. Machines, 10.","DOI":"10.3390\/machines10090736"},{"key":"ref_48","first-page":"1110","article-title":"Fault Diagnosis for Power Converters Based on Optimized Temporal Convolutional Network","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/6\/1884\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:56:01Z","timestamp":1760028961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/6\/1884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":48,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["s25061884"],"URL":"https:\/\/doi.org\/10.3390\/s25061884","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]}}}