Frequency domain tensor-based 1D-convolutional neural network and multilinear principal component analysis for machinery fault detection
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Published
Nov 5, 2024
Ayantha Senanayaka
Qing Lee
Nayeon Lee
Sungkwang Mun
Amin Amirlatifi
Joseph Jabour
Thomas Arnold
Maria Seale
Abstract
Challenges in detecting machinery faults, particularly in multivariate sensor environments, necessitate advanced feature extraction and classification techniques. This study introduces a novel approach that combines Multilinear Principal Component Analysis (MPCA) with a 1D-Convolutional Neural Network (1D-CNN) for efficient fault detection. By constructing Frequency Domain (FD) tensors from multivariate sensor data and applying MPCA for dimensionality reduction, our methodology enhances the capabilities of a 1D-CNN in feature learning and fault classification. The efficacy of this approach is validated through experiments on a Machinery Fault Simulator (MFS) with acoustic and vibration sensors, demonstrating notable improvements in fault detection accuracy compared to benchmark methods. The study results demonstrate that the proposed approach exhibits high accuracy in identifying machine fault conditions and outperforms the benchmark methods. The findings of this study have significant inferences for machine fault detection and fill the gap of more effective and reliable techniques in this domain.
How to Cite
Senanayaka Mudiyanselage, A., Lee, Q., Lee, N., Mun, S., Amirlatifi, A., Jabour, J., Arnold, T., & Seale, M. (2024). Frequency domain tensor-based 1D-convolutional neural network and multilinear principal component analysis for machinery fault detection. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3871
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Keywords
Predictive maintenance, Prognostic health monitoring, Real-time fault diagnosis, Condition monitoring, Rotating machinery faults, Multilinear principal component analysis, 1D-convolutional neural network
References
Aguileta, A. A., Brena, R. F., Mayora, O., Molino-Minero-re, E., & Trejo, L. A. (2019). Multi-sensor fusion for activity recognition—a survey. In Sensors (Switzerland) (Vol. 19, Issue 17). MDPI AG. https://doi.org/10.3390/s19173808
Al Mamun, A., Bappy, M. M., Mudiyanselage, A. S., Li, J., Jiang, Z., Tian, Z., Fuller, S., Falls, T. C., Bian, L., & Tian, W. (2023). Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis. International Journal of Advanced Manufacturing Technology, 124(3–4), 1321–1334. https://doi.org/10.1007/s00170-022-10525-4
Appana, D. K., Prosvirin, A., & Kim, J. M. (2018). Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Computing, 22(20), 6719–6729. https://doi.org/10.1007/s00500-018-3256-0
Banerjee, T. P., & Das, S. (2012). Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217, 96–107. https://doi.org/10.1016/j.ins.2012.06.016
Chen, G., Chen, J., Zi, Y., Pan, J., & Han, W. (2018). An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals. Sensors and Actuators, A: Physical, 269, 464–473. https://doi.org/10.1016/j.sna.2017.12.009
Chen, H., Hu, N., Cheng, Z., Zhang, L., & Zhang, Y. (2019). A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes. Measurement: Journal of the International Measurement Confederation, 146, 268–278. https://doi.org/10.1016/j.measurement.2019.04.093
Chen, S., Meng, Y., Tang, H., Tian, Y., He, N., & Shao, C. (2020). Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery. IEEE/ASME Transactions on Mechatronics, 25(5), 2167–2176. https://doi.org/10.1109/TMECH.2020.3007441
Chen, Z., & Li, W. (2017). Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702. https://doi.org/10.1109/TIM.2017.2669947
Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., & Lee, I.-B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems, 75(1), 55–67. https://doi.org/10.1016/j.chemolab.2004.05.001
Eren, L., Ince, T., & Kiranyaz, S. (2019). A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier. Journal of Signal Processing Systems, 91(2), 179–189. https://doi.org/10.1007/s11265-018-1378-3
Fu, Y., Gao, Z., Liu, Y., Zhang, A., & Yin, X. (2020). Actuator and sensor fault classification for wind turbine systems based on fast fourier transform and uncorrelated multi-linear principal component analysis techniques. Processes, 8(9). https://doi.org/10.3390/pr8091066
Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767. https://doi.org/10.1109/TIE.2015.2417501
Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F., & Madina, P. (2021). Data-driven fault diagnosis for electric drives: A review. In Sensors (Vol. 21, Issue 12). MDPI AG. https://doi.org/10.3390/s21124024
Guo, L., Gao, H., Huang, H., He, X., & Li, S. (2016). Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring. Shock and Vibration, 2016, 1–10. https://doi.org/10.1155/2016/4632562
Guo, Y., Zhou, Y., & Zhang, Z. (2021). Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis. Measurement: Journal of the International Measurement Confederation, 171. https://doi.org/10.1016/j.measurement.2020.108513
Hoang, D.-T., & Kang, H.-J. (2017). Convolutional Neural Network Based Bearing Fault Diagnosis. In D.-S. Huang, K.-H. Jo, & J. C. Figueroa-García (Eds.), Intelligent Computing Theories and Application (pp. 105–111). Springer International Publishing. https://doi.org/10.1007/978-3-319-63312-1_9
Hu, C., He, S., & Wang, Y. (2021). A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Applied Intelligence, 51(4), 2609–2621. https://doi.org/10.1007/s10489-020-02011-9
Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075. https://doi.org/10.1109/TIE.2016.2582729
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van De Walle, R., & Van Hoecke, S. (2016). Convolutional Neural Network Based Fault Detection for Rotating Machinery. Journal of Sound and Vibration, 377, 331–345. https://doi.org/10.1016/j.jsv.2016.05.027
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Significance of sensors for industry 4.0: Roles, capabilities, and applications. In Sensors International (Vol. 2). KeAi Communications Co. https://doi.org/10.1016/j.sintl.2021.100110
Jiao, J., Zhao, M., Lin, J., & Ding, C. (2019). Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis. IEEE Transactions on Industrial Electronics, 66(12), 9858–9867. https://doi.org/10.1109/TIE.2019.2902817
Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36–63. https://doi.org/10.1016/j.neucom.2020.07.088
Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement: Journal of the International Measurement Confederation, 111, 1–10. https://doi.org/10.1016/j.measurement.2017.07.017
Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 374, Issue 2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202
Kalsoom, T., Ramzan, N., Ahmed, S., & Ur-Rehman, M. (2020). Advances in sensor technologies in the era of smart factory and industry 4.0. In Sensors (Switzerland) (Vol. 20, Issue 23, pp. 1–22). MDPI AG. https://doi.org/10.3390/s20236783
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151. https://doi.org/10.1016/j.ymssp.2020.107398
Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., & Gabbouj, M. (2019). 1-D Convolutional Neural Networks for Signal Processing Applications. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 8360–8364. https://doi.org/10.1109/ICASSP.2019.8682194
Kullu, O., & Cinar, E. (2022). A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults. Machines, 10(11). https://doi.org/10.3390/machines10111105
Le Bris, A., Chehata, N., Ouerghemmi, W., Wendl, C., Postadjian, T., Puissant, A., & Mallet, C. (2019). Chapter 11 - Decision Fusion of Remote-Sensing Data for Land Cover Classification. In M. Y. Yang, B. Rosenhahn, & V. Murino (Eds.), Multimodal Scene Understanding (pp. 341–382). Academic Press. https://doi.org/10.1016/B978-0-12-817358-9.00017-2
Lee, K. B., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 30(2), 135–142. https://doi.org/10.1109/TSM.2017.2676245
Liton Hossain, M., Abu-Siada, A., & Muyeen, S. M. (2018). Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review. Energies, 11(5). https://doi.org/10.3390/en11051309
Liu, X., Ma, L., & Mathew, J. (2009). Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques. Mechanical Systems and Signal Processing, 23(3), 690–700. https://doi.org/10.1016/j.ymssp.2008.07.012
Liu, Y., Yan, X., Zhang, C. A., & Liu, W. (2019). An ensemble convolutional neural networks for bearing fault diagnosis using multi-sensor data. Sensors (Switzerland), 19(23). https://doi.org/10.3390/s19235300
Lu, H., Plataniotis, K. N., & Venetsanopoulos, A. N. (2008). MPCA: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks, 19(1), 18–39. https://doi.org/10.1109/TNN.2007.901277
Ma, P., Zhang, H., Fan, W., Wang, C., Wen, G., & Zhang, X. (2019). A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network. Measurement Science and Technology, 30(5). https://doi.org/10.1088/1361-6501/ab0793
Mallegni, N., Molinari, G., Ricci, C., Lazzeri, A., La Rosa, D., Crivello, A., & Milazzo, M. (2022). Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare. In Biosensors (Vol. 12, Issue 10). MDPI. https://doi.org/10.3390/bios12100835
Nallusamy, S., & Majumdar, G. (2017). Enhancement of Overall Equipment Effectiveness using Total Productive Maintenance in a Manufacturing Industry. In International Journal of Performability Engineering (Vol. 13, Issue 2).
Niu, G., Han, T., Yang, B.-S., & Tan, A. C. C. (2007). Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing, 21(3), 1285–1299. https://doi.org/10.1016/j.ymssp.2006.03.003
Paynabar, K., Jin, J. (Judy), & Pacella, M. (2013). Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Transactions, 45(11), 1235–1247. https://doi.org/10.1080/0740817X.2013.770187
Senanayaka, A., Al Mamun, A., Bond, G., Tian, W., Wang, H., Fuller, S., Falls, T. C., Rahimi, S., & Bian, L. (2022). Similarity-based Multi-source Transfer Learning Approach for Time Series Classification. International Journal of Prognostics and Health Management, 13, 1–9. https://doi.org/10.36001/IJPHM.2021.v13i2.3267
Shuang, L., & Meng, L. (2007). Bearing Fault Diagnosis Based on PCA and SVM. 2007 International Conference on Mechatronics and Automation, 3503–3507. https://doi.org/10.1109/ICMA.2007.4304127
Souza, R. M., Nascimento, E. G. S., Miranda, U. A., Silva, W. J. D., & Lepikson, H. A. (2021). Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 153, 107060. https://doi.org/10.1016/j.cie.2020.107060
Tripathi, S., Muhr, D., Brunner, M., Jodlbauer, H., Dehmer, M., & Emmert-Streib, F. (2021). Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing. In Frontiers in Artificial Intelligence (Vol. 4). Frontiers Media S.A. https://doi.org/10.3389/frai.2021.576892
Wang, J., Xie, J., Zhao, R., Zhang, L., & Duan, L. (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing, 45, 47–58. https://doi.org/10.1016/j.rcim.2016.05.010
Xia, M., Li, T., Xu, L., Liu, L., & de Silva, C. W. (2018). Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Transactions on Mechatronics, 23(1), 101–110. https://doi.org/10.1109/TMECH.2017.2728371
You, K., Qiu, G., & Gu, Y. (2022). Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis. Sensors, 22(22). https://doi.org/10.3390/s22228906
Yu, F., Liao, L., Zhang, K., Xing, H., Zhao, Q., Zhang, L., & Luo, Z. (2022a). A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8986900
Yu, F., Liao, L., Zhang, K., Xing, H., Zhao, Q., Zhang, L., & Luo, Z. (2022b). A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8986900
Zhang, S., Wei, H.-L., & Ding, J. (2023). An effective zero-shot learning approach for intelligent fault detection using 1D CNN. Applied Intelligence, 53(12), 16041–16058. https://doi.org/10.1007/s10489-022-04342-1
Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 100, 439–453. https://doi.org/10.1016/j.ymssp.2017.06.022
Zoghlami, F., Kaden, M., Villmann, T., Schneider, G., & Heinrich, H. (2021). AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. Sensors (Basel, Switzerland), 21(13). https://doi.org/10.3390/s21134405
Al Mamun, A., Bappy, M. M., Mudiyanselage, A. S., Li, J., Jiang, Z., Tian, Z., Fuller, S., Falls, T. C., Bian, L., & Tian, W. (2023). Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis. International Journal of Advanced Manufacturing Technology, 124(3–4), 1321–1334. https://doi.org/10.1007/s00170-022-10525-4
Appana, D. K., Prosvirin, A., & Kim, J. M. (2018). Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Computing, 22(20), 6719–6729. https://doi.org/10.1007/s00500-018-3256-0
Banerjee, T. P., & Das, S. (2012). Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217, 96–107. https://doi.org/10.1016/j.ins.2012.06.016
Chen, G., Chen, J., Zi, Y., Pan, J., & Han, W. (2018). An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals. Sensors and Actuators, A: Physical, 269, 464–473. https://doi.org/10.1016/j.sna.2017.12.009
Chen, H., Hu, N., Cheng, Z., Zhang, L., & Zhang, Y. (2019). A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes. Measurement: Journal of the International Measurement Confederation, 146, 268–278. https://doi.org/10.1016/j.measurement.2019.04.093
Chen, S., Meng, Y., Tang, H., Tian, Y., He, N., & Shao, C. (2020). Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery. IEEE/ASME Transactions on Mechatronics, 25(5), 2167–2176. https://doi.org/10.1109/TMECH.2020.3007441
Chen, Z., & Li, W. (2017). Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702. https://doi.org/10.1109/TIM.2017.2669947
Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., & Lee, I.-B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems, 75(1), 55–67. https://doi.org/10.1016/j.chemolab.2004.05.001
Eren, L., Ince, T., & Kiranyaz, S. (2019). A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier. Journal of Signal Processing Systems, 91(2), 179–189. https://doi.org/10.1007/s11265-018-1378-3
Fu, Y., Gao, Z., Liu, Y., Zhang, A., & Yin, X. (2020). Actuator and sensor fault classification for wind turbine systems based on fast fourier transform and uncorrelated multi-linear principal component analysis techniques. Processes, 8(9). https://doi.org/10.3390/pr8091066
Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767. https://doi.org/10.1109/TIE.2015.2417501
Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F., & Madina, P. (2021). Data-driven fault diagnosis for electric drives: A review. In Sensors (Vol. 21, Issue 12). MDPI AG. https://doi.org/10.3390/s21124024
Guo, L., Gao, H., Huang, H., He, X., & Li, S. (2016). Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring. Shock and Vibration, 2016, 1–10. https://doi.org/10.1155/2016/4632562
Guo, Y., Zhou, Y., & Zhang, Z. (2021). Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis. Measurement: Journal of the International Measurement Confederation, 171. https://doi.org/10.1016/j.measurement.2020.108513
Hoang, D.-T., & Kang, H.-J. (2017). Convolutional Neural Network Based Bearing Fault Diagnosis. In D.-S. Huang, K.-H. Jo, & J. C. Figueroa-García (Eds.), Intelligent Computing Theories and Application (pp. 105–111). Springer International Publishing. https://doi.org/10.1007/978-3-319-63312-1_9
Hu, C., He, S., & Wang, Y. (2021). A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Applied Intelligence, 51(4), 2609–2621. https://doi.org/10.1007/s10489-020-02011-9
Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075. https://doi.org/10.1109/TIE.2016.2582729
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van De Walle, R., & Van Hoecke, S. (2016). Convolutional Neural Network Based Fault Detection for Rotating Machinery. Journal of Sound and Vibration, 377, 331–345. https://doi.org/10.1016/j.jsv.2016.05.027
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Significance of sensors for industry 4.0: Roles, capabilities, and applications. In Sensors International (Vol. 2). KeAi Communications Co. https://doi.org/10.1016/j.sintl.2021.100110
Jiao, J., Zhao, M., Lin, J., & Ding, C. (2019). Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis. IEEE Transactions on Industrial Electronics, 66(12), 9858–9867. https://doi.org/10.1109/TIE.2019.2902817
Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36–63. https://doi.org/10.1016/j.neucom.2020.07.088
Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement: Journal of the International Measurement Confederation, 111, 1–10. https://doi.org/10.1016/j.measurement.2017.07.017
Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 374, Issue 2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202
Kalsoom, T., Ramzan, N., Ahmed, S., & Ur-Rehman, M. (2020). Advances in sensor technologies in the era of smart factory and industry 4.0. In Sensors (Switzerland) (Vol. 20, Issue 23, pp. 1–22). MDPI AG. https://doi.org/10.3390/s20236783
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151. https://doi.org/10.1016/j.ymssp.2020.107398
Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., & Gabbouj, M. (2019). 1-D Convolutional Neural Networks for Signal Processing Applications. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 8360–8364. https://doi.org/10.1109/ICASSP.2019.8682194
Kullu, O., & Cinar, E. (2022). A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults. Machines, 10(11). https://doi.org/10.3390/machines10111105
Le Bris, A., Chehata, N., Ouerghemmi, W., Wendl, C., Postadjian, T., Puissant, A., & Mallet, C. (2019). Chapter 11 - Decision Fusion of Remote-Sensing Data for Land Cover Classification. In M. Y. Yang, B. Rosenhahn, & V. Murino (Eds.), Multimodal Scene Understanding (pp. 341–382). Academic Press. https://doi.org/10.1016/B978-0-12-817358-9.00017-2
Lee, K. B., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 30(2), 135–142. https://doi.org/10.1109/TSM.2017.2676245
Liton Hossain, M., Abu-Siada, A., & Muyeen, S. M. (2018). Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review. Energies, 11(5). https://doi.org/10.3390/en11051309
Liu, X., Ma, L., & Mathew, J. (2009). Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques. Mechanical Systems and Signal Processing, 23(3), 690–700. https://doi.org/10.1016/j.ymssp.2008.07.012
Liu, Y., Yan, X., Zhang, C. A., & Liu, W. (2019). An ensemble convolutional neural networks for bearing fault diagnosis using multi-sensor data. Sensors (Switzerland), 19(23). https://doi.org/10.3390/s19235300
Lu, H., Plataniotis, K. N., & Venetsanopoulos, A. N. (2008). MPCA: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks, 19(1), 18–39. https://doi.org/10.1109/TNN.2007.901277
Ma, P., Zhang, H., Fan, W., Wang, C., Wen, G., & Zhang, X. (2019). A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network. Measurement Science and Technology, 30(5). https://doi.org/10.1088/1361-6501/ab0793
Mallegni, N., Molinari, G., Ricci, C., Lazzeri, A., La Rosa, D., Crivello, A., & Milazzo, M. (2022). Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare. In Biosensors (Vol. 12, Issue 10). MDPI. https://doi.org/10.3390/bios12100835
Nallusamy, S., & Majumdar, G. (2017). Enhancement of Overall Equipment Effectiveness using Total Productive Maintenance in a Manufacturing Industry. In International Journal of Performability Engineering (Vol. 13, Issue 2).
Niu, G., Han, T., Yang, B.-S., & Tan, A. C. C. (2007). Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing, 21(3), 1285–1299. https://doi.org/10.1016/j.ymssp.2006.03.003
Paynabar, K., Jin, J. (Judy), & Pacella, M. (2013). Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Transactions, 45(11), 1235–1247. https://doi.org/10.1080/0740817X.2013.770187
Senanayaka, A., Al Mamun, A., Bond, G., Tian, W., Wang, H., Fuller, S., Falls, T. C., Rahimi, S., & Bian, L. (2022). Similarity-based Multi-source Transfer Learning Approach for Time Series Classification. International Journal of Prognostics and Health Management, 13, 1–9. https://doi.org/10.36001/IJPHM.2021.v13i2.3267
Shuang, L., & Meng, L. (2007). Bearing Fault Diagnosis Based on PCA and SVM. 2007 International Conference on Mechatronics and Automation, 3503–3507. https://doi.org/10.1109/ICMA.2007.4304127
Souza, R. M., Nascimento, E. G. S., Miranda, U. A., Silva, W. J. D., & Lepikson, H. A. (2021). Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 153, 107060. https://doi.org/10.1016/j.cie.2020.107060
Tripathi, S., Muhr, D., Brunner, M., Jodlbauer, H., Dehmer, M., & Emmert-Streib, F. (2021). Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing. In Frontiers in Artificial Intelligence (Vol. 4). Frontiers Media S.A. https://doi.org/10.3389/frai.2021.576892
Wang, J., Xie, J., Zhao, R., Zhang, L., & Duan, L. (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing, 45, 47–58. https://doi.org/10.1016/j.rcim.2016.05.010
Xia, M., Li, T., Xu, L., Liu, L., & de Silva, C. W. (2018). Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Transactions on Mechatronics, 23(1), 101–110. https://doi.org/10.1109/TMECH.2017.2728371
You, K., Qiu, G., & Gu, Y. (2022). Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis. Sensors, 22(22). https://doi.org/10.3390/s22228906
Yu, F., Liao, L., Zhang, K., Xing, H., Zhao, Q., Zhang, L., & Luo, Z. (2022a). A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8986900
Yu, F., Liao, L., Zhang, K., Xing, H., Zhao, Q., Zhang, L., & Luo, Z. (2022b). A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8986900
Zhang, S., Wei, H.-L., & Ding, J. (2023). An effective zero-shot learning approach for intelligent fault detection using 1D CNN. Applied Intelligence, 53(12), 16041–16058. https://doi.org/10.1007/s10489-022-04342-1
Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 100, 439–453. https://doi.org/10.1016/j.ymssp.2017.06.022
Zoghlami, F., Kaden, M., Villmann, T., Schneider, G., & Heinrich, H. (2021). AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. Sensors (Basel, Switzerland), 21(13). https://doi.org/10.3390/s21134405
Section
Poster Presentations
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