Peng Wang

Publications

Zhang, J., Wang, P., & Gao, R. X. (2021). Hybrid machine learning for human action recognition and prediction in assembly. Robotics and Computer-Integrated Manufacturing, 72 , 1-10.
Wang, P., Gao, R. X., & Woyczynski, W. X. (2020). Lévy process-based stochastic modeling for machine performance degradation prognosis. IEEE Transactions on Industrial Electronics, 68 (12), 12760-12770.
Xiong, Q., Zhang, J., Wang, P., Liu, D., & Gao, R. X. (2020). Transferable two-stream convolutional neural network for human action recognition. Journal of Manufacturing Systems, 56 , 605-614.
Shao, S., Yan, R., Lu, Y., Wang, P., & Gao, R. X. (2020). DCNN-based Multi-signal Induction Motor Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 69 (6), 2658-2669.
Grezmak, J., Zhang, J., Wang, P., Loparo, K. A., & Gao, R. X. (2020). Interpretable Convolutional Neural Network through Layer-wise Relevance Propagation for Machine Fault Diagnosis. IEEE Sensors Journal, 20 (6), 3172-3181.
Grezmak, J., Zhang, J., Wang, P., Loparo, K. A., & Gao, R. X. (2020). Interpretable Convolutional Neural Network through Layer-wise Relevance Propagation for Machine Fault Diagnosis. IEEE Sensors Journal, 20 (6), 3172-3181.
Zhao, D., Cheng, W., Gao, R. X., Yan, R. X., & Wang, P. X. (2020). Generalized Vold–Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement, 69 (2), 401-410.
Cooper, C., Wang, P., Zhang, J., Gao, R. X., Roney, T. X., Ragai, I. X., & Shaffer, D. X. (2020). Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals. Procedia Manufacturing, 49 , 105-111.
Cooper, C., Zhang, J., Gao, R. X., Wang, P. X., & Ragai, I. X. (2020). Anomaly detection in milling tools using acoustic signals and generative adversarial networks. Procedia Manufacturing, 48 , 372-378.
Grezmak, J., Zhang, J., Wang, P., & Gao, R. X. (2020). Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems. Procedia Manufacturing, 43 , 511-518.
Zhang, J., Wang, P., & Gao, R. X. (2020). Attention Mechanism-Incorporated Deep Learning for AM Part Quality Prediction. Procedia CIRP, 93 , 96-101.
Wang, P., & Gao, R. X. (2020). Transfer learning for enhanced machine fault diagnosis in manufacturing. CIRP Annals - Manufacturing Technology, 69 (1), 413-416.
Zhang, J., Wang, P., & Gao, R. X. (2019). Deep learning-based tensile strength prediction in fused deposition modeling. Computers in Industry, 107 , 11-21.
Wang, P., Liu, Z., Gao, R. X., & Guo, Y. X. (2019). Heterogeneous data-driven hybrid machine learning for tool condition prognosis. CIRP Annals - Manufacturing Technology.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115 , 213-237.
Zhang, J., Wang, P., Gao, R. X., Sun, C. X., & Yan, R. X. (2019). Induction Motor Condition Monitoring for Sustainable Manufacturing. Procedia Manufacturing, 33 , 802-809.
Wang, P., & Gao, R. X. (2019). Prognostic Modeling of Performance Degradation in Energy Storage by Lithium-ion Batteries. Procedia Manufacturing, 34 , 911-920.
Grezmak, J., Wang, P., Sun, C., & Gao, R. X. (2019). Explainable Convolutional Neural Network for Gearbox Fault Diagnosis. Procedia CIRP, 80 , 476-481.
Sun, C., Wang, P., Yan, R., Gao, R. X., & Chen, X. X. (2019). Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization. Mechanical Systems and Signal Processing, 114 , 25-34.
Zhao, D., Cheng, W., Gao, R. X., Yan, R. X., & Wang, P. X. (2019). Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement.
Shao, S., Yan, R., Lu, Y., Wang, P., & Gao, R. X. (2019). DCNN-based Multi-signal Induction Motor Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement.
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of Manufacturing Systems.
Zhang, J., Wang, P., Gao, R. X., & Yan, R. X. (2018). An Image Processing Approach to Machine Fault Diagnosis Based on Visual Words Representation. Procedia Manufacturing, 19 , 42-49.
Zhao, D., Li, J., Cheng, W., Wang, P., Gao, R. X., & Yan, R. X. (2018). Vold-Kalman generalized demodulation for multi-faults detection of gear and bearing under variable speeds. Procedia Manufacturing, 26 , 1213-1220.
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Deep Learning for Improved System Remaining Life Prediction. Procedia CIRP, 72 , 1033-1038.
Wang, P., Liu, H., Wang, L., & Gao, R. X. (2018). Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Annals - Manufacturing Technology, 67 (1), 17-20.
Wang, J., Zheng, Y., Wang, P., & Gao, R. X. (2017). A virtual sensing based augmented particle filter for tool condition prognosis. Journal of Manufacturing Processes.
Gao, R. X., & Wang, P. X. (2017). Through Life Analysis for Machine Tools: From Design to Remanufacture. Procedia CIRP, 59 , 2-7.
Wang, P., & Gao, R. X. (2017). Automated Performance Tracking for Heat Exchangers in HVAC. IEEE Transactions on Automation Science and Engineering, 14 (2), 634-645.
Wang, P., Ananya, P., Yan, R., & Gao, R. X. (2017). Virtualization and deep recognition for system fault classification. Journal of Manufacturing Systems.
Wang, P., Gao, R. X., & Yan, R. X. (2017). A deep learning-based approach to material removal rate prediction in polishing. CIRP Annals - Manufacturing Technology.
Wang, P., Fan, Z., Kazmer, D., & Gao, R. X. (2017). Orthogonal analysis of multi-sensor data fusion for improved quality control. Journal of Manufacturing Science and Engineering.
Xu, J., Luo, R., Wang, P., Gilmore, H., & Madabhushi, A. (2016). A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.. Neurocomputing, 191 , 214-223.
Wang, P., Gao, R. X., Tang, X. X., & Fan, Z. X. (2016). Sensing Uncertainty Evaluation for Product Quality. Procedia CIRP [22128271], 41 , 706-711.
Wang, P., & Gao, R. X. (2016). Stochastic Tool Wear Prediction for Sustainable Manufacturing. Procedia CIRP [22128271], 48 , 236-241.
Wang, P., & Gao, R. X. (2016). Markov Nonlinear System Estimation for Engine Performance Tracking. Journal of Engineering for Gas Turbines and Power [07424795], 138 (9).
Xu, J., Xiang, L., Wang, P., Ganesan, S., Feldman, M., Shih, N., Gilmore, H., & Madabhushi, A. (2015). Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 46 Pt 1 , 20-9.
Zhang, Y., Han, T., Zhu, L., Fang, J., Wang, P., Xu, J., Xu, P., Li, X., & Liu, C. (2015). Pt35Cu65 nanoarchitecture: a highly durable and effective electrocatalyst towards methanol oxidation. NANOTECHNOLOGY, 26 (13), -.
Wang, P., Gao, R. X., & Fan, Z. X. (2015). Cloud Computing for Cloud Manufacturing: Benefits and Limitations. , 137 (4), -.