Nonlinear Torsional Vibration Dynamics Behaviors of Rolling Mill's Multi-DOF Main Drive System under Parametric Excitation. - Dongying Han; Peiming Shi; Kewei Xia (J. Appl. Math., 2014)

Signal Detection Based on Second-Order Underdamped Tristable Stochastic Resonance and Its Application to Weak Fault Diagnosis. - Wenyue Zhang; Peiming Shi; Mengdi Li; Yongxu Jiao; Dongying Han (IEEE Access, 2019)

Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis. - Rongrong Fu; Yongsheng Tian; Tiantian Bao; Zong Meng; Peiming Shi (J. Medical Syst., 2019)

An Intelligent Fault Diagnosis Method of Variable Condition Gearbox Based on Improved DBN Combined With WPEE and MPE. - Dongying Han; Xiaoci Guo; Peiming Shi (IEEE Access, 2020)

Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task. - Rongrong Fu; Mengmeng Han; Fuwang Wang; Peiming Shi (J. Medical Syst., 2020)

Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm. - Rongrong Fu; Yongsheng Tian; Peiming Shi; Tiantian Bao (J. Medical Syst., 2020)

Feature Extraction Method for Rolling Bear Fault Signal Based on time-Delayed Feedback Asymmetric tristable Stochastic Resonance. - Xiao Su; Peiming Shi; Qing Guo; Xingguo Zhang; Haixia Yu; Dachao Li (I2MTC, 2021)

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions. - Peiming Shi; Peng Xue; Aoyun Liu; Dongying Han (IEEE Access, 2021)

Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks. - Rongrong Fu; Mengmeng Han; Tiantian Bao; Fuwang Wang; Peiming Shi (Int. J. Neural Syst., 2021)

Multi-scale deep coupling convolutional neural network with heterogeneous sensor data for intelligent fault diagnosis. - Jinghui Tian; Dongying Han; Lifeng Xiao; Peiming Shi (J. Intell. Fuzzy Syst., 2021)

A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis. - Jinghui Tian; Dongying Han; Mengdi Li; Peiming Shi (Knowl. Based Syst., 2022)

Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples. - Rongrong Peng; Xingzhong Zhang; Peiming Shi (Sensors, 2022)

Domain adaptation meta-learning network with discard-supplement module for few-shot cross-domain rotating machinery fault diagnosis. - Yu Zhang; Dongying Han; Jinghui Tian; Peiming Shi (Knowl. Based Syst., 2023)

Multi-Representation Domain Adaptation Network with Duplex Adversarial Learning for Hot-Rolling Mill Fault Diagnosis. - Rongrong Peng; Xingzhong Zhang; Peiming Shi (Entropy, 2023)

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise. - Shuai Zhao; Peiming Shi (Sensors, 2023)

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis. - Jinghui Tian; Dongying Han; Hamid Reza Karimi; Yu Zhang; Peiming Shi (Neural Networks, 2023)

Characteristic frequency detection of steady-state visual evoked potentials based on filter bank second-order underdamped tristable stochastic resonance. - Peiming Shi; Jinxia Li; Wenyue Zhang; Mengdi Li; Dongying Han (Biomed. Signal Process. Control., 2023)

TSN: A novel intelligent fault diagnosis method for bearing with small samples under variable working conditions. - Peiming Shi; Shuping Wu; Xuefang Xu; Bofei Zhang; Pengfei Liang; Zijian Qiao (Reliab. Eng. Syst. Saf., 2023)

A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery. - Xuefang Xu; Shuo Bao; Haidong Shao; Peiming Shi (Adv. Eng. Informatics, 2024)

Multi-source heterogeneous information fusion fault diagnosis method based on deep neural networks under limited datasets. - Dongying Han; Yu Zhang; Yue Yu; Jinghui Tian; Peiming Shi (Appl. Soft Comput., 2024)

Incremental forecaster using C-C algorithm to phase space reconstruction and broad learning network for short-term wind speed prediction. - Shiting Hu; Xuefang Xu; Mengdi Li; Peiming Shi; Ruixiong Li; Shuying Wang (Eng. Appl. Artif. Intell., 2024)

A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis. - Xuefang Xu; Shuo Bao; Pengfei Liang; Zijian Qiao; Changbo He; Peiming Shi (Expert Syst. Appl., 2024)

Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis. - Yu Zhang; Dongying Han; Peiming Shi (Expert Syst. Appl., 2024)

Rolling mill fault diagnosis under limited datasets. - Junjie He; Peiming Shi; Xuefang Xu; Dongying Han (Knowl. Based Syst., 2024)

Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions. - Xuefang Xu; Xu Yang; Zijian Qiao; Pengfei Liang; Changbo He; Peiming Shi (Knowl. Based Syst., 2024)

A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data. - Jinghui Tian; Dongying Han; Hamid Reza Karimi; Yu Zhang; Peiming Shi (Neural Networks, 2024)

A bearing remaining life prediction method under variable operating conditions based on cross-transformer fusioning segmented data cleaning. - Dongxiao Hou; Jiahui Chen; Rongcai Cheng; Xue Hu; Peiming Shi (Reliab. Eng. Syst. Saf., 2024)

Adversarial Domain Adaptation Model Based on LDTW for Extreme Partial Transfer Fault Diagnosis of Rotating Machines. - Xuefang Xu; Xu Yang; Changbo He; Peiming Shi; Changchun Hua (IEEE Trans. Instrum. Meas., 2024)

KMDSAN: A novel method for cross-domain and unsupervised bearing fault diagnosis. - Shuping Wu; Peiming Shi; Xuefang Xu; Xu Yang; Ruixiong Li; Zijian Qiao (Knowl. Based Syst., 2025)

Simulated Data-Assisted Fault Diagnosis Framework With Dual-Path Feature Fusion for Rolling Element Bearings Under Incomplete Data. - Xuefang Xu; Xiaofeng Ou; Liang Ge; Zijian Qiao; Peiming Shi (IEEE Trans. Instrum. Meas., 2025)