Cited by 233Machine Fault Diagnosis TechniquesFault Detection and Control SystemsElectrical Fault Detection and Protection

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

Open access

Guo, Sheng, Yang, Tao, Gao, Wei, Zhang, Chen·2018·Sensors

DOI 10.3390/s18051429PMC PMC5982639

Open this paper in CoChat — free account Open PDF

Abstract

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3390/s18051429

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646
Face recognition: a convolutional neural-network approach
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554195 · Cited by 3,092
MatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444
A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals
Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017) · Sensors · doi:10.3390/s17020425 · Cited by 1,925
Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life
Babu, G. S. D., Zhao, P., & Li, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32025-0_14 · Cited by 1,040
End-to-End Text Recognition with Convolutional Neural Networks
Wang, T., Wu, D. J., Coates, A., & Ng, A. Y. (2012) · Cited by 839
Application of the EEMD method to rotor fault diagnosis of rotating machinery
Lei, Y., He, Z., & Zi, Y. (2008) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2008.11.005 · Cited by 587
Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble
Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.01.007 · Cited by 402

Flagged first, then by citations

Show all 29 references

Research works printed but not linked: 1

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.

Sorted by citations

Items 101–200 of 233

Eigen-spectrograms: An interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing
Brusa, E. G. M., Delprete, C., & Maggio, L. G. D. (2022) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2022.2102274 · Cited by 20
Bolt-Loosening Detection Using 1D and 2D Input Data Based on Two-Stream Convolutional Neural Networks
Hou, X., Guo, W., Ren, S., Li, Y., Si, Y., & Su, L. (2022) · Materials · doi:10.3390/ma15196757 · Cited by 19
Research on an intelligent diagnosis method of mechanical faults for small sample data sets
Zhao, J., Shi, Y., Tan, F., Wang, X., Zhang, Y., Liao, J., Yang, F., & Guo, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-26316-6 · Cited by 20
Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs
van den Hoogen, J., Bloemheuvel, S., & Atzmueller, M. (2021) · Applied Sciences · doi:10.3390/app112311429 · Cited by 21
Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network
Zhang, B., Zhou, C., Li, W., Ji, S., Li, H., Tong, Z., & Ng, S.-K. (2022) · Mathematics · doi:10.3390/math10213953 · Cited by 20
An Improved Wide-Kernel CNN for Classifying Multivariate Signals in Fault Diagnosis
van den Hoogen, J., Bloemheuvel, S., & Atzmueller, M. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00046 · Cited by 18
Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks
Liu, X., Xia, L., Shi, J., Zhang, L., & Wang, S. (2023) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-023-00949-x · Cited by 18
Physics-guided self-supervised learning for rotating-machinery fault diagnosis: role of average energy density equation
Lee, J., Hong, S., Lee, S., & Lee, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103876 · Cited by 15
Chasing the Cut: A Measurement Approach for Machine Tool Condition Monitoring
Huchel, L., Krause, T. C., Lugowski, T., Leeb, S. B., & Helsen, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3047939 · Cited by 15
Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet TransformPreprint
Kaji, M., Parvizian, J., & van de Venn, H. W. (2020) · Preprints.org · doi:10.20944/preprints202011.0591.v1 · Cited by 15
An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network
Yan, Y., Wu, J., Cao, Y., Liu, B., Li, C., & Shi, T. (2024) · Sensors · doi:10.3390/s24061745 · Cited by 14
Bearing fault diagnosis using a speed-adaptive network based on vibro-speed data fusion and majority voting
Yuan, Z., Ma, Z., Li, X., Gao, D., & Fu, Z. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac46ee · Cited by 14
Vibration-based Condition Monitoring in Wind Turbine Gearbox Using Convolutional Neural Network
Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2022) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc53348.2022.9867477 · Cited by 15
Multi-scale deep learning framework for robust bearing fault diagnosis in rotating machinery under sensor anomalies
Pedram, R., & Chaibakhsh, A. (2025) · Journal of Control and Decision · doi:10.1080/23307706.2025.2567006 · Cited by 13
Integration of wavelet decomposition and artificial neural network for failure prognosis of reciprocating compressors
Lu, Y., & Wang, C. (2021) · Process Safety Progress · doi:10.1002/prs.12239 · Cited by 12
Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network
Sharma, P., Chandan, S., Shaw, R. N., & Ghosh, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85498-6.00011-3 · Cited by 12
Research on a Bearing Fault Enhancement Diagnosis Method with Convolutional Neural Network Based on Adaptive Stochastic Resonance
Wang, C., Qiao, Z., Huang, Z., Xu, J., Fang, S., Zhang, C., Liu, J., Zhu, R., & Lai, Z. (2022) · Sensors · doi:10.3390/s22228730 · Cited by 13
A Health Data Map-Based Ensemble of Deep Domain Adaptation Under Inhomogeneous Operating Conditions for Fault Diagnosis of a Planetary Gearbox
Ha, J. M., & Youn, B. D. (2021) · IEEE Access · doi:10.1109/access.2021.3083804 · Cited by 12
Unsupervised Condition Diagnosis of Linear Motion Guide Using Generative Model Based on Images
Hong, D., Bang, S., & Kim, B. (2021) · IEEE Access · doi:10.1109/access.2021.3084602 · Cited by 12
Condition Monitoring in a Wind Turbine Planetary Gearbox Using Sensor Fusion and Convolutional Neural Network
Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.11.276 · Cited by 15
A time series and deep fusion framework for rotating machinery fault diagnosis
Zhang, J., Hu, D., Yang, T., Zhou, H., & Li, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107456 · Cited by 10
Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network
Luo, T., Qiu, M., Wu, Z., Zhao, Z., & Zhang, D. (2025) · Journal of Vibroengineering · doi:10.21595/jve.2025.24934 · Cited by 11
Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition
Liu, D., Lai, X., Xiao, Z., Liu, D., Hu, X., & Zhang, P. (2020) · Shock and Vibration · doi:10.1155/2020/6542913 · Cited by 11
Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis
Fu, C., Lv, Q., & Lin, H. (2020) · Shock and Vibration · doi:10.1155/2020/8837958 · Cited by 11
Detection of tyre defects using weighted quality-based convolutional neural network
Rajeswari, M., Julie, E. G., Robinson, Y. H., Joseph, E., Arun, A., Sebastian, E., Kumar, R., Long, H. V., & Sơn, L. H. (2022) · Soft Computing · doi:10.1007/s00500-022-06878-3 · Cited by 11
Vibration Fault Analysis of Hydropower Units Based on Extreme Learning Machine Optimized by Improved Sparrow Search Algorithm
Kong, F., Song, C., & Zhuo, Y. (2022) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-022-00660-3 · Cited by 11
A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions
Wang, Z., Xu, X., Zhang, Y., Wang, Z., Li, Y., Liu, Z., & Zhang, Y. (2023) · Sensors · doi:10.3390/s23156730 · Cited by 11
Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM
Zhang, X., Pan, X., Zeng, H., & Zhou, H. (2024) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-024-01046-0 · Cited by 10
Research on Damage Detection Methods for Concrete Beams Based on Ground Penetrating Radar and Convolutional Neural Networks
Liu, N., Ge, Y., Bai, X. W., Zhang, Z. M., Shangguan, Y., & Yan, L. (2025) · Applied Sciences · doi:10.3390/app15041882 · Cited by 10
Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network
Yang, X., Jiang, A., Jiang, W., Zhao, Y., Tang, E., & Chang, S. (2024) · Machines · doi:10.3390/machines12060368 · Cited by 9
Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network
Wu, B., Feng, S., Sun, G., Xu, L., & Ai, C. (2019) · Journal of Vibroengineering · doi:10.21595/jve.2019.20359 · Cited by 9
Recent Advances of Neural Network based Methods in Induction Motor Fault Diagnosis
Karnavas, Y. L., Chasiotis, I. D., Drakaki, M., & Tziafettas, I. A. (2020) · International Conference on Electrical Machines (ICEM) · doi:10.1109/icem49940.2020.9270873 · Cited by 9
Mechanical Fault Diagnosis Methods Based on Convolutional Neural Network: a Review
Zhang, T., & Dai, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1750/1/012048 · Cited by 9
Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs
Hendriks, J., & Dumond, P. (2021) · Vibration · doi:10.3390/vibration4020019 · Cited by 9
A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot
Ma, L., Guo, J., Li, J., & Wang, J. (2022) · Unmanned Systems · doi:10.1142/s2301385023410042 · Cited by 9
Cycle Condition Identification of Loader Based on Optimized KNN Algorithm
Zhang, Z., Hui, J., Shi, Z., Gao, S., Zhang, X., & Fan, H. (2020) · IEEE Access · doi:10.1109/access.2020.2985052 · Cited by 9
An Uncertainty-Aware Health Monitoring Model for Wind Turbine Drivetrains Based On Bayesian Neural Network
Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.12.030 · Cited by 9
A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery
Wang, G., Zhao, Y., Zhang, J., & Ning, Y. (2021) · Sensors · doi:10.3390/s21062056 · Cited by 8
Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network
Sun, G., Yang, X., Xiong, C., Hu, Y., & Liu, M. (2022) · Applied Sciences · doi:10.3390/app12104831 · Cited by 8
Sandpiper Optimization with a Deep Learning Enabled Fault Diagnosis Model for Complex Industrial Systems
Duhayyim, M. A., Mohamed, H. G., Alzahrani, J. S., Alabdan, R., Aziz, A. S. A., Zamani, A. S., Yaseen, I., & Alsaid, M. I. (2022) · Electronics · doi:10.3390/electronics11244190 · Cited by 8
Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study
van den Hoogen, J., Hudson, D., Bloemheuvel, S., & Atzmueller, M. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00440-6 · Cited by 11
A Bayesian deep learning framework for reliable fault diagnosis in wind turbine gearboxes under various operating conditions
Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2023) · Wind Engineering · doi:10.1177/0309524x231206723 · Cited by 9
Fractal Lifting Wavelets for Machine Fault Diagnosis
Chen, B., Li, Y., Zeng, N., & He, W. (2019) · IEEE Access · doi:10.1109/access.2019.2908213 · Cited by 8
Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning
Xiang, G., & Tian, K. (2021) · International Journal of Aerospace Engineering · doi:10.1155/2021/6099818 · Cited by 8
Small-Sample Fault Diagnosis of Axial Piston Pumps across Working Conditions, Based on 1D-SENet Model Migration
Yang, X., Jiang, A., Jiang, W., Yue, Y., Jing, L., & Zhou, J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12081430 · Cited by 7
Bearing Fault Detection based on Internet of Things using Convolutional Neural Network
Chakraborty, S., Shamrat, F. M. J. M., Ahammad, R., Billah, M. M., Kabir, M., & Hosen, M. R. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130424 · Cited by 7
Agriculture Rotary Tedder Fault Diagnosis Based on Evolutionary Convolutional Neural Network with Genetic Algorithm Optimization
Sewiolo, M., & Mystkowski, A. (2023) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar58394.2023.10242439 · Cited by 7
Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis
Niu, G., Zhang, B., Ziehl, P. H., Ferrese, F. T., & Golda, E. M. (2019) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2019.v11i1.882 · Cited by 7
Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System
Pang, X., Shao, J., Xue, X., & Jiang, W. (2020) · International Journal of Rotating Machinery · doi:10.1155/2020/9540791 · Cited by 7
A Rolling Bearing Fault Diagnosis Method Based on Switchable Normalization and a Deep Convolutional Neural Network
Han, X., Cao, Y., Luan, J., Ao, R., Feng, W., & Li, S. (2023) · Machines · doi:10.3390/machines11020185 · Cited by 8
Fault diagnosis of hydraulic actuator based on improved convolutional neural network
Zhao, L., Wang, S., Shi, J., & Zhang, C. (2020) · Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) · doi:10.1109/aparm49247.2020.9209471 · Cited by 6
Accompanying deep framework for faults in motor and gearbox with disproportion vibrational samples
Karamti, H., Lashin, M. M. A., Alrowais, F., & Mahmoud, A. M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08020-8 · Cited by 6
A novel method for online prediction of the remaining useful life of rolling bearings based on wavelet power spectrogram and Transformer structure
Guo, X., Tu, J., Zhan, S., Zhang, W., Ma, L., & Jia, D. (2023) · Engineering Research Express · doi:10.1088/2631-8695/ad08fc · Cited by 6
Application of Composite Spectrum in Agricultural Machines
García, F. F., Gómez-Gil, F. J., & Gil, J. (2020) · Sensors · doi:10.3390/s20195519 · Cited by 7
Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning
Sun, G., Hu, Y., Wu, B., & Zhou, H. (2021) · Shock and Vibration · doi:10.1155/2021/8456991 · Cited by 6
Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
Calderon-Uribe, U., Lizarraga-Morales, R. A., & Guryev, I. V. (2023) · Applied Sciences · doi:10.3390/app13106137 · Cited by 6
Lightweight Gearbox Fault Diagnosis Under High Noise Based on Improved Multi-Scale Depthwise Separable Convolution and Efficient Channel Attention
Liu, X., Li, W., Li, H., Zhu, Y., & Agarwal, R. K. (2026) · Sensors · doi:10.3390/s26041196 · Cited by 7
Study on Methods Using Multi-Label Learning for the Classification of Compound Faults in Auxiliary Equipment Pumps of Marine Engine Systems
Yu, B., Kim, Y., Lee, T. H., Cho, Y., Park, J., Lee, J.-J., & Park, J. (2024) · Processes · doi:10.3390/pr12102161 · Cited by 5
Early detection and localization of stator inter turn faults based on variational mode decomposition and deep learning in induction motor
Guedidi, A., Laala, W., Guettaf, A., & Arif, A. (2023) · Diagnostyka · doi:10.29354/diag/174001 · Cited by 5
Research on the Identification of Bridge Structural Damage Using Variational Mode Decomposition and Convolutional Self-Attention Neural Networks
Liu, Q., Nie, P., Dai, H., Ning, L., & Wang, J. (2023) · Applied Sciences · doi:10.3390/app132112082 · Cited by 5
Rotating Machinery Fault Diagnosis Using One-Dimensional Convolutional Neural Network
Kibrete, F., Woldemichael, D. E., & Gebremedhen, H. S. (2025) · Green energy and technology · doi:10.1007/978-3-031-77339-6_5 · Cited by 8
Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm
Jiang, P., Cong, H., Wang, J., & Zhang, D. (2020) · Shock and Vibration · doi:10.1155/2020/9238908 · Cited by 5
Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions
Ban, H., Wang, D., Wang, S., & Liu, Z. (2021) · Sensors · doi:10.3390/s21093226 · Cited by 5
Vibration Parameters for Impact Detection of Composite Panel: A Neural Network Based Approach
Arena, M., & Viscardi, M. (2021) · Journal of Composites Science · doi:10.3390/jcs5070185 · Cited by 5
Actuator Fault Diagnosis in Multi-Zone HVAC Systems using 2D Convolutional Neural Networks
Elnour, M., & Meskin, N. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089508 · Cited by 5
Symmetric Radial Vectors for uncertainty-aware rotary machinery fault diagnosis
Gu, J., Yuan, X., Li, X., Wang, Y., & Xing, J. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111639 · Cited by 4
A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
Jiaqi, T. I. A. N., & Gu, B. (2024) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2024jamdsm0012 · Cited by 4
A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis
Zhang, Y., Ye, J., Yang, W., Shi, J., He, W., & Cai, G. (2022) · Shock and Vibration · doi:10.1155/2022/8552388 · Cited by 4
Fault detection and isolation in uncertain dynamic systems using composite optimization and inferential sensing
Safikou, E., & Bollas, G. M. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108509 · Cited by 5
Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process
Choi, N.-H., Sohn, J. W., & Oh, J.-S. (2023) · Mathematics · doi:10.3390/math11244894 · Cited by 5
Fault Diagnosis of Rotating Machinery under Complex Conditions Based on Multi-Scale Convolutional Neural Networks
Kang, J., Luo, Y., Wang, P., Wei, Y., & Zhou, Y. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2658/1/012038 · Cited by 4
Crack detection in rotating shafts using combined wavelet analysis
Pricop, M., Pazara, T., Pricop, C., & Novac, G. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1297/1/012031 · Cited by 4
Fault diagnosis of single point cutting tool using spectrum, cepstrum and wavelet analysis
Aralikatti, S. S., Ravikumar, K. N., & Kumar, H. (2019) · AIP Conference Proceedings · doi:10.1063/1.5141218 · Cited by 3
Transfer Learning‐Based Domain‐Adaptive One‐Dimensional Convolutional Neural Network for Fault Diagnosis of Rotating Machines
Kibrete, F., Woldemichael, D. E., & Gebremedhen, H. S. (2025) · Engineering Reports · doi:10.1002/eng2.70258 · Cited by 3
Adversarial autoencoding Crossformer model with transfer learning for remaining service life prediction of rolling bearings
Xiong, J., Li, F., Tang, B., Wang, Y., Luo, L., Wang, Y., & Tian, D. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae26b7 · Cited by 3
AI-Enabled End-of-Line Quality Control in Electric Motor Manufacturing: Methods, Challenges, and Future Directions
Mlinarič, J., & Dolanč, G. (2026) · Machines · doi:10.3390/machines14020149 · Cited by 3
Fault diagnosis and predictive maintenance of electric vehicle powertrain using artificial intelligence techniques: A review
Kumar, M. P., Velpula, S., & Saiprakash, C. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110857 · Cited by 8
A Fault Diagnosis Method of Rolling Bearing Based on Convolutional Neural Network
Zhang, B., Gao, S., Hu, G., Gao, Z. W., Zhao, Y., & Du, J. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10327400 · Cited by 3
Symmetrical Loss of Excitation Fault Diagnosis in an Asynchronized High-Voltage Generator
Lv, Y., Gao, Y., Zhang, J., Deng, C., & Hou, S. (2018) · Energies · doi:10.3390/en11113054 · Cited by 3
Modeling and Intelligent Identification of Axis Orbit for Rotating Machinery Based on the Convolution Neural Networks
He, X., Liu, X., Lu, X., He, L., Ma, Y., Guo, S., & Yang, T. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1746/1/012011 · Cited by 3
Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features
Alghassi, A. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-67270-6_7 · Cited by 3
Fault Detection on Bearings and Rotating Machines based on Vibration Sensors Data
Kotsiopoulos, T., Vafeiadis, T., Apostolidis, A., Nizamis, A., Alexopoulos, N., Ioannidis, D., Tzovaras, D. K., & Sarigiannidis, P. G. (2021) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic53636.2021.9686999 · Cited by 3
MANN-BiLSTM: A Memory-Augmented Neural Network for Multi-Class Bearing Fault Detection
Kırdar, A., Hardalaç, F., & Ayturan, K. (2025) · doi:10.1109/ubmk67458.2025.11206997 · Cited by 2
Rolling bearing fault diagnosis method based on improved multi-scale residual networks and light gradient boosting machine
Wang, L., Wang, Z., Wu, Z., Yang, X., & Xie, Y. (2026) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-025-06071-1 · Cited by 2
MPHLDAE‐1DCNN: A Novel Denoising Method for Improved Fault Diagnosis
Kibrete, F., Woldemichael, D. E., & Gebremedhen, H. S. (2026) · International Journal of Rotating Machinery · doi:10.1155/ijrm/7592976 · Cited by 2
A Multi-Scale Residual Attention Network for Drainage Pump Fault Diagnosis
Song, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3664497 · Cited by 2
Ponder: Enabling Balloon-Borne Based Solar Unmanned Aerial Vehicle’s Take Off Diagnosis under Little Data
Hu, Y., Jiao, Y., Shang, Y., Li, S., & Hu, Y. (2022) · Entropy · doi:10.3390/e24070997 · Cited by 2
Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation
Nan, G., Wang, J., & Ding, D. (2023) · Journal of Vibroengineering · doi:10.21595/jve.2023.23040 · Cited by 2
Unknown Fault Detection of Rolling Bearing Based on Similarity Mining of Stationary and Non-stationary Features
Li, R. W., Nie, J., Wang, C., Niu, D., Yu, S., Nie, W., & Ding, X. (2023) · International Workshop on Human-centric Multimedia Analysis (HCMA) · doi:10.1145/3606041.3618063 · Cited by 2
A Multi-Scale Deep Learning Attention-based Feature Method for Rolling Elements Bearing Fault Detection in Industrial Motor Drives
Karnavas, Y. L., Plakias, S., & Chasiotis, I. D. (2021) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast52088.2021.9493397 · Cited by 2
Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network
Hong, J., & Tian, W. (2023) · Processes · doi:10.3390/pr11051443 · Cited by 2
Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet TransformPreprint
Kaji, M., Parvizian, J., & van de Venn, H. W. (2020) · Preprints.org · doi:10.20944/preprints202011.0591.v2 · Cited by 2
Logic System Design for Fault Detection and Classification of Voltage Source Inverter Driving Induction Motor
Mahafzah, K. A., & Obeidat, M. A. (2022) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2022.16.133 · Cited by 1
Leveraging systems’ non-linearity to tackle the scarcity of data in the design of intelligent fault diagnosis systems
Santamato, G., Garavagno, A. M., Solazzi, M., & Frisoli, A. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-09864-6 · Cited by 1
Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia
Refadah, S. S., AlAbadi, S., Almazroui, M., Khan, M. A. H., ElKashouty, M., & Khan, M. Y. A. (2025) · Technologies · doi:10.3390/technologies13100461 · Cited by 1
Intelligent Fault Diagnosis Method of Mechanical Fault Based on Wavelet Transform
Wang, D. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0115-6_229 · Cited by 1
A Lightweight Residual Network and Its Application in Bearing Fault Diagnosis
Zhang, F., Yin, Z., Li, J., Xu, G., & Xu, F. (2023) · IEEE International Conference on Communication Software and Networks · doi:10.1109/iccsn57992.2023.10297373 · Cited by 1
Fault Diagnosis for Rolling Bearings Using Time-Frequency Energy Matrix Difference and CNN
Li, L., Yuan, X., & Yang, C. (2023) · International Conference on Mechanical and Electronics Engineering · doi:10.1109/icmee59781.2023.10525623 · Cited by 1
A containerized Edge AI predictive maintenance infrastructure for rotating components using continuous wavelet transform images and Transformer UNet autoencoder for Industry 4.0
Keshavarz, M. A., & Sharifian, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07904-5 · Cited by 1
Signal processing and machine learning techniques based hybrid approaches for decent fault classification of induction motor
Panigrahy, P. S., & Chattopadhyay, P. (2026) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-026-03215-x · Cited by 1

Authors: Guo, S., Yang, T., Gao, W., & Zhang, C.
Affiliations: Huazhong University of Science and Technology (CN)
Corresponding author: Yang, Tao
ORCID: Guo, Sheng
Yang, Tao
Gao, Wei
Zhang, Chen
Venue: Sensors
Publisher: MDPI AG
Published: 2018-05-04
Volume: 18
Issue: 5
Pages: 1429
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s18051429
PMC: PMC5982639
OpenAlex: W2801457104
Cited by: 233
References: 29
Citations by year: 2026: 16 · 2025: 17 · 2024: 20 · 2023: 34 · 2022: 30 · 2021: 45 · 2020: 36 · 2019: 30 · 2018: 5
FWCI: 17.95
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Chinese National Science and Technology Support Program (2015BAA06B02)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
Keywords: fault diagnosis, rotating machinery, convolutional neural networks, vibration signals, rotor fault diagnosis, time-frequency analysis, feature extraction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks