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Sai Ma - IEEE Xplore Author Profile
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Sai Ma

Publications
5
Citations
34
Publications by Year
20212025
Co-Authors:
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Sai Ma

Affiliation

School of Mechanical Engineering
Shandong University
Jinan, China
Key Laboratory of High-Efficiency and Clean Mechanical Manufacture
Shandong University Ministry of Education
Jinan, China

Publication Topics

Biography

Sai Ma received the Ph.D. degree in mechanical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2016.,He was a Research Assistant with the Department of Mechanical Engineering, Tsinghua University, Beijing, China. In 2018, he joined the School of Mechanical Engineering, Shandong University, Jinan, China, where he is currently an Associate Researcher. His research interests include predictive maintenance of mechanical equipment, mechanical signal processing, and artificial intelligence.(Based on document published on 20 November 2024).
Publications
5
Citations
34
Publications by Year
20212025
Co-Authors:
Author's Published Works

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Deep Transfer Learning With Generalized Distribution Matching Measure for Rotating Machinery Fault Diagnosis

Peng Zhu;Sai Ma;Qinkai Han;Fulei Chu

IEEE Transactions on Industrial Informatics
Year: 2025 | Early Access Article |
Diagnostic models based on deep transfer learning hold the potential to apply diagnostic knowledge across relevant machinery. However, existing methods suffer from several drawbacks. First, the random initialization of convolutional kernels lacks interpretability and may lead to suboptimal solutions, affecting diagnostic accuracy and training convergence. Second, treating all channels equally limi...Show More

Deep Transfer Learning With Generalized Distribution Matching Measure for Rotating Machinery Fault Diagnosis

Peng Zhu;Sai Ma;Qinkai Han;Fulei Chu

Year: 2025 | Early Access Article |

Deep Contrastive Transfer Learning for Rotating Machinery Fault Diagnosis

Peng Zhu;Sai Ma;Qinkai Han;Fulei Chu

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Volume: 74 | Journal Article |
Diagnostic models based on deep transfer learning hold the potential to apply diagnostic knowledge across relevant machinery. However, the existing methods suffer from several drawbacks: 1) most methods use wavelet convolutional kernels to extract features, but the extracted features are redundant and may diminish the diagnostic accuracy; 2) traditional domain alignment methods cannot effectively ...Show More

Deep Contrastive Transfer Learning for Rotating Machinery Fault Diagnosis

Peng Zhu;Sai Ma;Qinkai Han;Fulei Chu

Year: 2025 | Volume: 74 | Journal Article |

Unified Sparse Time–Frequency Analysis: Decomposition, Transformation, and Reassignment

Li Wang;Sai Ma;Qinkai Han;Fulei Chu

IEEE Transactions on Industrial Informatics
Year: 2022 | Volume: 18, Issue: 11 | Journal Article |
Cited by: Papers (5)
Time–frequency (TF) analysis is essential for industrial engineering applications. However, the conventional TF analysis methods suffer from blurry TF energy. This article proposes a new unified sparse TA analysis (STFA) framework to concentrate the blurry energy, restrain noise, separate condition-related components, and retain the signal reconstruction property. The STFA framework leverages the ...Show More

Unified Sparse Time–Frequency Analysis: Decomposition, Transformation, and Reassignment

Li Wang;Sai Ma;Qinkai Han;Fulei Chu

Year: 2022 | Volume: 18, Issue: 11 | Journal Article |

Enhanced Sparse Low-Rank Representation via Nonconvex Regularization for Rotating Machinery Early Fault Feature Extraction

Li Wang;Sai Ma;Qinkai Han

IEEE/ASME Transactions on Mechatronics
Year: 2022 | Volume: 27, Issue: 5 | Journal Article |
Cited by: Papers (21)
The weak fault feature extraction is key to early fault diagnosis of rotary machinery. However, the existing sparse low-rank fault feature extraction methods have the deficiency of underestimation and low peak signal-to-noise ratio. To solve these problems, this article presents an enhanced sparse low-rank (ESL) representation approach for weak fault feature extraction. Considering the periodic se...Show More

Enhanced Sparse Low-Rank Representation via Nonconvex Regularization for Rotating Machinery Early Fault Feature Extraction

Li Wang;Sai Ma;Qinkai Han

Year: 2022 | Volume: 27, Issue: 5 | Journal Article |

Reweighted Dual Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Li Wang;Sai Ma;Qinkai Han

IEEE Transactions on Instrumentation and Measurement
Year: 2021 | Volume: 70 | Journal Article |
Cited by: Papers (8)
Performing early and accurate bearing fault diagnosis is of great significance. However, accurate extraction of the repetitive transients from noisy vibration signals is a critical issue. In this article, a reweighted dual sparse regularization (RDSR) method is proposed for bearing fault diagnosis, and the RDSR is a unified framework of the reweighted generalized minimax-concave (reGMC) penalty an...Show More

Reweighted Dual Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Li Wang;Sai Ma;Qinkai Han

Year: 2021 | Volume: 70 | Journal Article |

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