{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:16:44Z","timestamp":1773796604200,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation for Young Scholars","award":["No.51907138"],"award-info":[{"award-number":["No.51907138"]}]},{"name":"National Natural Science Foundation of Chinal","award":["No.51777132"],"award-info":[{"award-number":["No.51777132"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As the core component of the valve cooling system in a converter station, the main pump plays a major role in ensuring the stable operation of the valve. Thus, accurate and efficient fault diagnosis of the main pump according to vibration signals is of positive significance for the detection of failure equipment and reducing the maintenance cost. This paper proposed a new neural network based on the vibration signals of the main pump to classify four faults and one normal state of the main pump, which consisted of a convolutional neural network (CNN) and long short-term memory (LSTM). Multi-scale features were extracted by two CNNs with different kernel sizes, and temporal features were extracted by LSTM. Moreover, random sampling was used in data processing for imbalanced data, which is meaningful for data symmetry. Experimental results indicated that the accuracy of the network was 0.987 obtained from the test set, and the average values of F1-score, recall, and precision were 0.987, 0.987, and 0.988, respectively. It was found that the proposed network performed well in a multi-label fault diagnosis of the main pump and was superior to other methods.<\/jats:p>","DOI":"10.3390\/sym13071284","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T21:18:52Z","timestamp":1626643132000},"page":"1284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Fault Diagnosis of Main Pump in Converter Station Based on Deep Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Qingsheng","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gong","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Han","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingkang","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","unstructured":"Holweg, J., Lips, H.P., Tu, B.Q., Uder, M., Peng, B., and Zhang, Y. (2002, January 13\u201317). Modern HVDC thyristor valves for China\u2019s electric power system. Proceedings of the Proceedings. International Conference on Power System Technology, Kunming, China."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"588","DOI":"10.4028\/www.scientific.net\/AMM.614.588","article-title":"Hybrid Cooling System for HVDC Convertor Valves","volume":"3391","author":"Wen","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_3","first-page":"1157","article-title":"Research on the Formation and Preventive Measure of Scale in the Cooling System of HVDC Converter Valve","volume":"354","author":"Qian","year":"2012","journal-title":"Adv. Mater. Res."},{"key":"ref_4","unstructured":"ShaoHua, Y., Qibin, T., Maozhong, W., and Luxiang, Z. (2015, January 28\u201329). Cooling System Circuit Analysis of \u00b1800 kV DC Power Transmission Converter Valve. Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering, Hangzhou, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, A., Guan, S., Geng, M., and Cui, P. (2020, January 6\u20139). Fault analysis and optimized improvement for main pump of HVDC Converter Stations valve cooling system. Proceedings of the 2020 4th International Conference on HVDC (HVDC), Xi\u2019an, China.","DOI":"10.1109\/HVDC50696.2020.9292791"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.measurement.2017.04.041","article-title":"Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump","volume":"108","author":"Kumar","year":"2017","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.21595\/jve.2017.18120","article-title":"Vibration-based classification of centrifugal pumps using support vector machine and discrete wavelet transform","volume":"19","author":"Ebrahimi","year":"2017","journal-title":"J. Vibroeng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/JSEN.2018.2804908","article-title":"Cyclic Spectral Analysis of Vibration Signals for Centrifugal Pump Fault Characterization","volume":"18","author":"Hui","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s40430-018-1202-9","article-title":"Automation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domain","volume":"40","author":"Rapur","year":"2018","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_10","first-page":"854","article-title":"Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT","volume":"22","author":"Bevan","year":"2019","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106809","DOI":"10.1016\/j.measurement.2019.07.037","article-title":"Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses","volume":"147","author":"Rapur","year":"2019","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"223030","DOI":"10.1109\/ACCESS.2020.3044195","article-title":"Multistage Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Modes of Vibration and Using Pearson Linear Discriminant Analysis","volume":"8","author":"Ahmad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165512","DOI":"10.1109\/ACCESS.2020.3022770","article-title":"Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis","volume":"8","author":"Ahmad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s10033-017-0190-5","article-title":"Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network","volume":"30","author":"Wang","year":"2017","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_16","first-page":"439","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Wei","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107399","DOI":"10.1016\/j.apacoust.2020.107399","article-title":"Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images","volume":"167","author":"Kumar","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"580972","DOI":"10.1155\/2014\/580972","article-title":"Robust fault detection of wind energy conversion systems based on dynamic neural networks","volume":"2014","author":"Talebi","year":"2014","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.engappai.2015.01.016","article-title":"Methodology of neural modelling in fault detection with the use of chaos engineering","volume":"41","author":"Moczulski","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.isatra.2016.01.002","article-title":"Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system","volume":"61","author":"Mrugalski","year":"2016","journal-title":"ISA Trans."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","unstructured":"Ioffffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_23","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_24","first-page":"369","article-title":"Artificial neural network\u2013based internal leakage fault detection for hydraulic actuators: An experimental investigation","volume":"232","author":"Yao","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part I J. Syst. Control. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheemala, V., Asokan, A.N., and Preetha, P. (2019, January 21\u201323). Transformer Incipient Fault Diagnosis using Machine Learning Classifiers. Proceedings of the 2019 IEEE 4th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Chennai, India.","DOI":"10.1109\/CATCON47128.2019.CN0046"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"74793","DOI":"10.1109\/ACCESS.2020.2989371","article-title":"An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis under Noise and Varying Load Conditions","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isatra.2020.12.034","article-title":"Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance","volume":"114","author":"Chen","year":"2020","journal-title":"ISA Trans."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dey, R., and Salemt, F.M. (2017, January 6\u20139). Gate-variants of Gated Recurrent Unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref_29","unstructured":"Yi, B., Yang, Y., Huang, Z., Shen, F., and Shen, H.T. (2016). Bidirectional Long-Short Term Memory for Video Description. ACM, 436\u2013440."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xie, X. (2017, January 28\u201331). Opinion Expression Detection via Deep Bidirectional C-GRUs. Proceedings of the International Workshop on Database & Expert Systems Applications, Lyon, France.","DOI":"10.1109\/DEXA.2017.40"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/7\/1284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:56Z","timestamp":1760164256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/7\/1284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["sym13071284"],"URL":"https:\/\/doi.org\/10.3390\/sym13071284","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]}}}