2025

• Ma Z, Zhao M, Dai X, et al. "Anomaly detection for high-speed machining using hybrid regularized support vector data description," Robotics and Computer-Integrated Manufacturing,vol. 94, p. 102962, 2025.
• Zhang Y, Zhao M, Li S, et al. ACKgram: A robust approach for bearing fault diagnosis under complex interference," Mechanical Systems and Signal Processing, vol. 230, p. 112646, 2025.
• Li S, Zhao M, Song Z, et al. Adaptive product cyclic spectrum and its application for fault diagnosis of rotating machinery," Measurement Science and Technology, vol. 36, p. 066123, 2025.

2024

• Ou S, Zhao M, Wu H, et al. "Machinery degradation trend prediction considering temporal distribution discrepancy between degradation stages," Engineering Applications of Artificial Intelligence, vol. 131, p. 107872, 2024.

• Ma Z, Zhao M, Dai X, et al. "Compound fault diagnosis of wind turbine bearing under ultra-low speed operations using generalized sparse spectral coherence, " Mechanical Systems and Signal Processing, vol. 208, p. 111027, 2024.

• Ma Z, Zhang Y, Dai X, et al. "Kernel Density Regularized Bayesian Learning Framework for Machining Process Anomaly Detection, " Artificial Intelligence Technologies and Applications (AITA), p. 207, 2024.

• Z. Ma, M. Zhao, S. Ou, B. Ma and Y. Zhang, "Physics-Inspired Sparse Voiceprint Sensing for Bearing Fault Diagnosis", IEEE Transactions on Industrial Informatics, p. 1-12，2024.

• S. Ou, M. Zhao, B. Li, Y. Zhang, S. Li and H. Wu, "Tensor Low-Rank and Sparse Decomposition and Its Application in Bearing Fault Information Separation", Measurement Science and Technology, 2024.

• S. Ou, M. Zhao, X. Cheng, S. Li and Z. Song, "A Novel Bearing Health Sensing Technique Using Smartphone", Measurement, vol. 235, p. 114972, 2024.

• Li S, Zhao M, Wei Y, et al. "Adaptive residual spectral amplitude modulation: A new approach for bearing diagnosis under complex interference environments", Mechanical Systems and Signal Processing, vol. 220, p. 111682, 2024.

2023

• Li S, Zhao M, Ou S, et al. "A periodic anomaly detection framework based on matrix profile for condition monitoring of planetary gearboxes," Measurement, vol. 218, p. 113243, 2023.
• Z. Ma, M. Zhao, X. Dai, Y. Chen, "A hybrid-driven probabilistic state space model for tool wear monitoring," Mechanical Systems and Signal Processing, vol. 200, p. 110599, 2023.
• S. Li, S. Ou, D. Chen, L. Wu, X. Han and M. Zhao. "A MP-based Method for Periodic Fault Impulses Detection in Rotating Machinery," 2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou), p. 1, 2023.
• Ma Z, Zhao M, Dai X, et al. "Optimized Spectral Amplitude Modulation based on Generalized Envelope for Bearing Compound Fault Diagnosis, " 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing(ICMSP), p. 310, 2023
• H. Wu, S. Ou, S. Li, X. Cheng, B. Cui and M. Zhao. "Reweighted Dynamic Mode Decomposition for Fault Feature Extraction of Rolling Element Bearings," 2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou), p. 1, 2023.

2022

• X. Xu, W. Li, M. Zhao, H. Hu, " Mobile device-based bearing diagnostics with varying speeds," Measurement, p. 111639, 2022.

• Z. Ma, M. Zhao, M. Luo, C. Gou, G. Xu,''An integrated monitoring scheme for wind turbine main bearing using acoustic emission'', Signal Processing, vol.205, 2023

• S. Ou, M. Zhao, S. Li, T. Zhou, "Online shock sensing for rotary machinery using encoder signal," Mechanical Systems and Signal Processing, vol. 182, p. 109599, 2023.

• S. Ou, S. Li, C. Wu, M. Luo and M. Zhao, "A health self-sensing framework for electromechanical equipment using encoder signal," 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control ( SDPC), p. 67-70, 2022. “Best Paper Award”

• W. Xu, H. Tan, M. Zhao, "An improved multipoint optimal minimum entropy deconvolution adjusted method for the diagnosis of rotating machinery under variable speed conditions," Proceedings of the Institution of Mechanical Engineers.
• C. Ding, M. Zhao, J. Lin, K. Liang, J. Jiao, "Kernel Ridge Regression-Based Chirplet Transform for Non-Stationary Signal Analysis and Its Application in Machine Fault Detection Under Varying Speed Conditions," Measurement, p. 110871, 2022.
• Y. Miao, B. Zhang, J. Lin, M. Zhao, H. Liu, Z. Liu, H. Li, "A review on the application of blind deconvolution in machinery fault diagnosis," Mechanical Systems and Signal Processing, vol. 163, p. 108202, 2022.
• J. Jiao, J. Lin, M. Zhao, K. Liang, C. Ding, "Cycle-consistent Adversarial Adaptation Network and its application to machine fault diagnosis," Neural Networks, vol. 145, pp. 331-341, 2022.

2021

• S. Ou, M. Zhao, T. Zhou, and D. Guo, "An encoder signal-based approach for low-speed planetary gearbox fault diagnosis," Measurement Science and Technology, vol. 32, no. 5, p. 054005, 2021.
• Y. Miao, B. Zhang, M. Zhao, and J. Lin, "Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis," ISA transactions, 2021.
• Z. Ma, M. Zhao, B. Li, and H. Fan, "A novel blind deconvolution based on sparse subspace recoding for condition monitoring of wind turbine gearbox," Renewable Energy, vol. 170, pp. 141-162, 2021.
• K. Liang, M. Zhao, J. Lin, J. Jiao, and C. Ding, "Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery," Mechanical Systems and Signal Processing, vol. 149, p. 107323, 2021.
• J. Jiao, M. Zhao, J. Lin, K. Liang, and C. Ding, "A mixed adversarial adaptation network for intelligent fault diagnosis," Journal of Intelligent Manufacturing, pp. 1-16, 2021.
• J. Jiao, M. Zhao, and J. Lin, "Multi-weight Domain Adversarial Network for Partial-set Transfer Diagnosis," IEEE Transactions on Industrial Electronics, 2021.
• Z. Ma, M. Zhao, S. Chen, D. Guo, "Encoder-based weak fault detection for rotating machinery using improved Gaussian process regression," Structural Health Monitoring, vol. 20, no. 1, pp. 255-272, 2021.
• K. Liang, M. Zhao, J. Lin, J. Jiao, C. Ding, "Toothwise health monitoring of planetary gearbox under time-varying speed condition based on rotating encoder signal," IEEE Transactions on Industrial Electronics, 2021.
• C. Yan, M. Zhao, J. Lin, C. Ding, "Segmentation MED method based on kurtosis-frequency curve and its application in bearing diagnosis," Measurement Science and Technology, vol. 32, no. 11, p. 115004, 2021.
• Z. Ma, M. Zhao, C. Gou, "Health Monitoring of Rotating Machinery Using Probabilistic Time Series Model," IEEE Transactions on Instrumentation and Measurement, 2021.

2020

• M. Zhao and Z. Ma, "From Polynomial Fitting to Kernel Ridge Regression: A Generalized Difference Filter for Encoder Signal Analysis," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6212-6220, 2020.
• M. Zhao, Y. Li, S. Chen, and B. Li, "Missing value recovery for encoder signals using improved low-rank approximation," Mechanical Systems and Signal Processing, vol. 139, p. 106595, 2020.
• C. Yan, M. Zhao, J. Lin, K. Liang, and Z. Zhang, "Fault signature enhancement and skidding evaluation of rolling bearing based on estimating the phase of the impulse envelope signal," Journal of Sound and Vibration, vol. 485, p. 115529, 2020.
• Y. Miao, M. Zhao, K. Liang, and J. Lin, "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, vol. 151, pp. 192-203, 2020.
• Y. Miao, M. Zhao, and J. Hua, "Research on sparsity indexes for fault diagnosis of rotating machinery," Measurement, vol. 158, p. 107733, 2020.
• Z. Ma, M. Zhao, S. Chen, and D. Guo, "Encoder-based weak fault detection for rotating machinery using improved Gaussian process regression," Structural Health Monitoring, p. 1475921720929755, 2020.
• K. Liang, M. Zhao, J. Lin, and J. Jiao, "An information-based K-singular-value decomposition method for rolling element bearing diagnosis," ISA transactions, vol. 96, pp. 444-456, 2020.
• J. Jiao, M. Zhao, J. Lin, and K. Liang, "Residual joint adaptation adversarial network for intelligent transfer fault diagnosis," Mechanical Systems and Signal Processing, vol. 145, p. 106962, 2020.
• J. Jiao, M. Zhao, J. Lin, and K. Liang, "A comprehensive review on convolutional neural network in machine fault diagnosis," Neurocomputing, vol. 417, pp. 36-63, 2020.
• J. Jiao, J. Lin, M. Zhao, and K. Liang, "Double-level adversarial domain adaptation network for intelligent fault diagnosis," Knowledge-Based Systems, vol. 205, p. 106236, 2020.
• C. Ding, M. Zhao, J. Lin, B. Wang, and K. Liang, "Transient feature extraction of encoder signal for condition assessment of planetary gearboxes with variable rotational speed," Measurement, vol. 151, p. 107206, 2020.
• C. Ding, M. Zhao, and J. Lin, "Sparse feature extraction based on periodical convolutional sparse representation for fault detection of rotating machinery," Measurement Science and Technology, vol. 32, no. 1, p. 015008, 2020.

2019

• M. Zhao, J. Jiao, and J. Lin, "A data-driven monitoring scheme for rotating machinery via self-comparison approach," IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2435-2445, 2019.
• L. Qu, J. Lin, Y. Liao, and M. Zhao, "Changes in rotor response characteristics based diagnostic method and its application to identification of misalignment," Measurement, vol. 138, pp. 91-105, 2019.
• Y. Miao, M. Zhao, V. Makis, and J. Lin, "Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal," Mechanical Systems and Signal Processing, vol. 122, pp. 673-691, 2019.
• Y. Miao, M. Zhao, and J. Lin, "Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition," ISA transactions, vol. 84, pp. 82-95, 2019.
• J. Jiao, M. Zhao, J. Lin, and K. Liang, "Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings," Reliability Engineering & System Safety, vol. 184, pp. 41-54, 2019.
• J. Jiao, M. Zhao, J. Lin, and C. Ding, "Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9858-9867, 2019.
• J. Jiao, M. Zhao, J. Lin, and C. Ding, "Classifier inconsistency-based domain adaptation network for partial transfer intelligent diagnosis," IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 5965-5974, 2019.
• J. Jiao, M. Zhao, and J. Lin, "Unsupervised adversarial adaptation network for intelligent fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9904-9913, 2019.
• C. Ding, M. Zhao, J. Lin, J. Jiao, and K. Liang, "Sparsity-Based Algorithm for Condition Assessment of Rotating Machinery Using Internal Encoder Data," IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7982-7993, 2019.
• C. Ding, M. Zhao, J. Lin, and J. Jiao, "Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings," ISA transactions, vol. 88, pp. 199-215, 2019.

​​​​​​2018

• M. Zhao, X. Jia, J. Lin, Y. Lei, and J. Lee, "Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox," Mechanical Systems and Signal Processing, vol. 98, pp. 16-31, 2018.
• L. Qu, Y. Liao, J. Lin, and M. Zhao, "Investigation on the subsynchronous pseudo-vibration of rotating machinery," Journal of Sound and Vibration, vol. 423, pp. 340-354, 2018.
• Y. Miao, M. Zhao, and J. Lin, "Periodicity-impulsiveness spectrum based on singular value negentropy and its application for identification of optimal frequency band," IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3127-3138, 2018.
• J. Jiao, M. Zhao, J. Lin, and J. Zhao, "A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes," Knowledge-Based Systems, vol. 160, pp. 237-250, 2018.
• X. Jia, M. Zhao, Y. Di, Q. Yang, and J. Lee, "Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy," IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5872-5881, 2018.
• X. Jia, M. Zhao, Y. Di, P. Li, and J. Lee, "Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery," Mechanical Systems and Signal Processing, vol. 102, no. Supplement C, pp. 198-213, 2018.
• X. Jia, M. Zhao, Y. Di, P. Li, and J. Lee, "Sparse filtering with the generalized lp / lq norm and its applications to the condition monitoring of rotating machinery," Mechanical Systems and Signal Processing, vol. 102, pp. 198-213, 2018.

​​​​​​2017

• M. Zhao and J. Lin, "Health assessment of rotating machinery using a rotary encoder," IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2548-2556, 2017.
• M. Zhao and X. Jia, "A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery," Mechanical Systems and Signal Processing, vol. 94, pp. 129-147, 2017.
• X. Xu, M. Zhao, and J. Lin, "Detecting weak position fluctuations from encoder signal using singular spectrum analysis," ISA transactions, vol. 71, pp. 440-447, 2017.
• Y. Miao, M. Zhao, J. Lin, and Y. Lei, "Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings," Mechanical Systems and Signal Processing, vol. 92, pp. 173-195, 2017.
• Y. Miao, M. Zhao, and J. Lin, "Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification," Measurement Science and Technology, vol. 28, no. 12, p. 125001, 2017.
• G. Lang, J. Lin, Y. Liao, and M. Zhao, "Impact of system anisotropy on vibration reduction of rotating machinery and its evaluation method," Mechanical Systems and Signal Processing, vol. 93, pp. 299-311, 2017.
• L. Jing, M. Zhao, P. Li, and X. Xu, "A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox," Measurement, vol. 111, pp. 1-10, 2017.
• L. Jing, T. Wang, M. Zhao, and P. Wang, "An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox," vol. 17, no. 2, p. 414, 2017.
• X. Jia, M. Zhao, Y. Di, Q. Yang, and J. Lee, "Assessment of data suitability for machine prognosis using maximum mean discrepancy," IEEE transactions on industrial electronics, vol. 65, no. 7, pp. 5872-5881, 2017.
• X. Jia, M. Zhao, Y. Di, C. Jin, and J. Lee, "Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement," Journal of Sound and Vibration, vol. 386, pp. 433-448, 2017.
• X. Jia, M. Zhao, M. Buzza, Y. Di, and J. Lee, "A geometrical investigation on the generalized lp/lq norm for blind deconvolution," Signal Processing, vol. 134, pp. 63-69, 2017.

​​​​​​2016

• M. Zhao, J. Lin, Y. Miao, and X. Xu, "Feature mining and health assessment for gearboxes using run-up/coast-down signals," Sensors, vol. 16, no. 11, p. 1837, 2016.
• M. Zhao, J. Lin, Y. Miao, and X. Xu, "Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings," Measurement, vol. 91, pp. 421-439, 2016.
• L. Zeng, M. Zhao, J. Lin, and W. Wu, "Waveform separation and image fusion for Lamb waves inspection resolution improvement," NDT & E International, vol. 79, pp. 17-29, 2016.
• M. Yonghao, Z. Ming, L. Jing, and X. Xiaoqiang, "Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings," Measurement Science and Technology, vol. 27, no. 10, p. 105004, 2016.
• X. Xu, M. Zhao, J. Lin, and Y. Lei, "Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis," Measurement, vol. 91, pp. 385-397, 2016.

2016以前

• X. Xu, M. Zhao, J. Lin, and Y. Lei, "Periodicity-based kurtogram for random impulse resistance," Measurement Science and Technology, vol. 26, no. 8, p. 085011, 2015.
• 赵明, 林京, 廖与禾, 曹军义, "基于自适应短时Chirp-Fourier变换的瞬时转速估计及应用," 机械工程学报, no. 14, pp. 8-14, 2015.
• 林京, 赵明, "变转速下机械设备动态信号分析方法的回顾与展望," 中国科学. E技术科学, vol. 07期, pp. 669-686, 2015.
• M. Zhao, L. Zeng, J. Lin, and W. Wu, "Mode identification and extraction of broadband ultrasonic guided waves," Measurement Science and Technology, vol. 25, no. 11, p. 115005, 2014.
• M. Zhao, J. Lin, X. Xu, and X. Li, "Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis," Sensors, vol. 14, no. 11, pp. 20320-20346, 2014.
• M. Zhao, J. Lin, X. Xu, and Y. Lei, "Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds," Sensors, vol. 13, no. 8, pp. 10856-10875, 2013.
• M. Zhao, J. Lin, X. Wang, Y. Lei, and J. Cao, "A tacho-less order tracking technique for large speed variations," Mechanical Systems and Signal Processing, vol. 40, no. 1, pp. 76-90, 2013.
• M. Zhao, J. Lin, Y. Lei, and X. Wang, "Flexible time domain averaging technique," Chinese Journal of Mechanical Engineering, vol. 26, no. 5, pp. 1022-1030, 2013.
• J. Lin and M. Zhao, "Research progress and prospects on machinery monitoring under varying working condition," 中国工程科学（英文版）, vol. 11, no. 1, pp. 29-34, 2013.
• 赵明, 林京, 王琇峰等, "基于数字微分器的瞬时转速波动分析及在振动溯源中的应用," 机械工程学报, vol. 48, no. 22, pp. 1-6, 2012.​​​​​