{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:46:20Z","timestamp":1784738780167,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In the industrial sector, accurate fault identification is paramount for ensuring both safety and economic efficiency throughout the production process. However, due to constraints imposed by actual working conditions, the motor state features collected are often limited in number and singular in nature. Consequently, extending and extracting these features pose significant challenges in fault diagnosis. To address this issue and strike a balance between model complexity and diagnostic accuracy, this paper introduces a novel motor fault diagnostic model termed FSCL (Fourier Singular Value Decomposition combined with Long and Short-Term Memory networks). The FSCL model integrates traditional signal analysis algorithms with deep learning techniques to automate feature extraction. This hybrid approach innovatively enhances fault detection by describing, extracting, encoding, and mapping features during offline training. Empirical evaluations against various state-of-the-art techniques such as Bayesian Optimization and Extreme Gradient Boosting Tree (BOA-XGBoost), Whale Optimization Algorithm and Support Vector Machine (WOA-SVM), Short-Time Fourier Transform and Convolutional Neural Networks (STFT-CNNs), and Variational Modal Decomposition-Multi Scale Fuzzy Entropy-Probabilistic Neural Network (VMD-MFE-PNN) demonstrate the superior performance of the FSCL model. Validation using the Case Western Reserve University dataset (CWRU) confirms the efficacy of the proposed technique, achieving an impressive accuracy of 99.32%. Moreover, the model exhibits robustness against noise, maintaining an average precision of 98.88% and demonstrating recall and F1 scores ranging from 99.00% to 99.89%. Even under conditions of severe noise interference, the FSCL model consistently achieves high accuracy in recognizing the motor\u2019s operational state. This study underscores the FSCL model as a promising approach for enhancing motor fault diagnosis in industrial settings, leveraging the synergistic benefits of traditional signal analysis and deep learning methodologies.<\/jats:p>","DOI":"10.3390\/info15070399","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T11:33:22Z","timestamp":1720697602000},"page":"399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD"],"prefix":"10.3390","volume":"15","author":[{"given":"Muzi","family":"Xu","sequence":"first","affiliation":[{"name":"College of Engineering, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Engineering, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7677-4211","authenticated-orcid":false,"given":"Shichao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhui","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Engineering, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.21595\/jve.2019.20092","article-title":"Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions","volume":"21","author":"Gougam","year":"2019","journal-title":"J. Vibroeng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bazurto, A.J., Quispe, E.C., and Mendoza, R.C. (2016, January 19\u201321). Causes and failures classification of industrial electric motor. Proceedings of the 2016 IEEE ANDESCON, Arequipa, Peru.","DOI":"10.1109\/ANDESCON.2016.7836190"},{"key":"ref_3","first-page":"1291","article-title":"Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors","volume":"13","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108002","DOI":"10.1016\/j.measurement.2020.108002","article-title":"A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings","volume":"164","author":"Rai","year":"2020","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11265-018-1378-3","article-title":"A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier","volume":"91","author":"Eren","year":"2019","journal-title":"J. Signal Process. Syst."},{"key":"ref_6","first-page":"233","article-title":"Study on denoising algorithm of phase-sensitive optical time-domain reflectometer based on moving variance averaging algorithm","volume":"43","author":"Guan","year":"2022","journal-title":"J. Instrum."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"139086","DOI":"10.1109\/ACCESS.2019.2940769","article-title":"Motor fault detection and feature extraction using RNN-based variational autoencoder","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"157796","DOI":"10.1109\/ACCESS.2019.2950240","article-title":"A fault diagnostic method for induction motors based on feature incremental broad learning and singular value decomposition","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","first-page":"137","article-title":"An improved adaptive momentum gradient descent algorithm","volume":"51","author":"Jiang","year":"2023","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103378","DOI":"10.1016\/j.compind.2020.103378","article-title":"Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms","volume":"125","author":"Jalayer","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","article-title":"Fault diagnosis of wind turbine based on Long Short-term memory networks","volume":"133","author":"Lei","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-time motor fault detection by 1-D convolutional neural networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","article-title":"Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders","volume":"77","author":"Liu","year":"2018","journal-title":"ISA Trans."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109529","DOI":"10.1016\/j.measurement.2021.109529","article-title":"A deep sequence multi-distribution adversarial model for bearing abnormal condition detection","volume":"182","author":"Ou","year":"2021","journal-title":"Measurement"},{"key":"ref_15","first-page":"659","article-title":"Application of improved LSTM neural network in very short-term wave time series forecasting","volume":"57","author":"Shang","year":"2023","journal-title":"J. Shanghai Jiao Tong Univ."},{"key":"ref_16","first-page":"20","article-title":"Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor","volume":"17","author":"Islam","year":"2018","journal-title":"Int. J. Inf. Technol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"155598","DOI":"10.1109\/ACCESS.2021.3128669","article-title":"Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4628462","DOI":"10.1155\/2022\/4628462","article-title":"Data Amplification for Bearing Remaining Useful Life Prediction Based on Generative Adversarial Network","volume":"2022","author":"Lei","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ifacol.2018.09.582","article-title":"Rotating machinery fault diagnosis using long-short-term memory recurrent neural network","volume":"51","author":"Yang","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, S., Qian, S., Chang, W., Xiao, Y., and Cheng, Y. (2018). A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier. Sensors, 18.","DOI":"10.3390\/s18061934"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.neucom.2018.12.041","article-title":"Exploiting the generative adversarial framework for one-class multi-dimensional fault detection","volume":"332","author":"Plakias","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4031795","DOI":"10.1155\/2019\/4031795","article-title":"Research on novel bearing fault diagnosis method based on improved krill herd algorithm and kernel extreme learning machine","volume":"2019","author":"Wang","year":"2019","journal-title":"Complexity"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, H., Hu, X., and Wang, W. (2024). Rolling bearing fault diagnosis based on multi-domain features and whale optimized support vector machine. J. Vib. Control.","DOI":"10.1177\/10775463241231344"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gu, X., Tian, Y., Li, C., Wei, Y., and Li, D. (2024). Improved SE-ResNet Acoustic\u2013Vibration Fusion for Rolling Bearing Composite Fault Diagnosis. Appl. Sci., 14.","DOI":"10.3390\/app14052182"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/7\/399\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:15:02Z","timestamp":1760109302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/7\/399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,11]]},"references-count":24,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["info15070399"],"URL":"https:\/\/doi.org\/10.3390\/info15070399","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,11]]}}}