副教授
性别：男
在职信息：在职
所在单位：机械工程学院
入职时间：2018-11-26
访问量：
最后更新时间：..
-
[1]
杨长远.
基于多核监督流形学习的旋转机械故障诊断.
航空动力学报,
1-8,
2023.
-
[2]
杨长远.
Unified discriminant manifold learning for rotating machinery fault diagnosis.
JOURNAL OF INTELLIGENT MANUFACTURING,
3483,
2023.
-
[3]
马赛.
Sparse representation learning for fault feature extraction and diagnosis of rotating machinery.
Expert Systems with Applications,
2023.
-
[4]
杨长远.
Robust discriminant latent variable manifold learning for rotating machinery fault diagnosis.
Engineering Applications of Artificial Intelligence,
2023.
-
[5]
杨长远.
Robust discriminant latent variable manifold learning for rotating machinery fault diagnosis.
Engineering Applications of Artificial Intelligence,
126,
2023.
-
[6]
杨长远.
Unified discriminant manifold learning for rotating machinery fault diagnosis.
JOURNAL OF INTELLIGENT MANUFACTURING,
34,
3483,
2023.
-
[7]
马赛.
Sparse representation learning for fault feature extraction and diagnosis of rotating machinery.
Expert Systems with Applications,
232,
2023.
-
[8]
.
Robust discriminant latent variable manifold learning for rotating machinery fault diagnosis.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,
2023.
-
[9]
马赛.
Sparse representation learning for fault feature extraction and diagnosis of rotating machinery.
Expert Systems with Applications,
2023.
-
[10]
马赛.
Unified discriminant manifold learning for rotating machinery fault diagnosis.
JOURNAL OF INTELLIGENT MANUFACTURING,
2022.
-
[11]
马赛.
Unified Sparse Time Frequency Analysis: Decomposition, Transformation and Reassignment.
IEEE Transactions on Industrial Informatics,
2022.
-
[12]
王力.
Enhanced Sparse Low-rank Representation via Nonconvex Regularization for Rotating Machinery Early Fault Feature Extraction.
IEEE/ASME TRANSACTIONS ON MECHATRONICS,
27,
2022.
-
[13]
王力.
Reweighted Dual Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis.
《IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT》,
70,
2020.
-
[14]
Han, Qinkai.
Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS,
115,
2019.
-
[15]
马赛.
Uncertainty reduced novelty detection approach applied to rotating machinery for condition monitoring.
Shock and vibration,
2015.
-
[16]
马赛.
Uncertainty extraction based multi-fault diagnosis of rotating machinery.
Journal of VIBROENGINEERING,
2016.
-
[17]
马赛.
Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions.
Mechanical Systems and Signal Processing,
127,
190,
2019.
-
[18]
马赛.
Ensemble deep learning-based fault diagnosis of rotor bearing systems.
COMPUTERS IN INDUSTRY,
105,
143,
2019.
-
[19]
马赛.
风速时间序列模拟的模型有效性验证及代表性风场实例分析.
《振动与冲击》,
2019.
-
版权所有 ©山东大学 地址：中国山东省济南市山大南路27号 邮编：250100
查号台：（86）-0531-88395114
值班电话：（86）-0531-88364731 建设维护：山东大学信息化工作办公室
-
手机版
登录 | 山东大学
