Sai Ma马赛

Shandong University | SDU · Department of Mechanical Design, Manufacturing and Automation

For rotating machinery, early and accurate diagnosis of rotor and bearing component fault is of great significance. The classic fault diagnosis model includes two key modules, feature extraction and fault classification. In order to enhance the practicability, the deep learning models realize the end-to-end fault diagnosis by integrating this two m...

Due to the tough and time-varying working conditions, fault diagnosis technique is of critical significance for drive-chain system in rotating machines. In recent years, many statistical and spectral feature extraction methods have been developed and applied, but unfortunately, they are incapable of dealing with mechanical behaviors under varying r...

Time-frequency (TF) analysis is essential for industrial engineering applications. However, the conventional TF analysis methods suffer from blurry TF energy. This paper proposes a new unified sparse time-frequency analysis (STFA) framework to concentrate the blurry energy, restrain noise, separate condition-related components and also retain the s...

Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively and accurately perf...

Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively perform feature ext...

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 fau...

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 reweigh...

A kernel density estimation (KDE) model for the probability distribution of wind speed (PDWS) is proposed in this paper for application to wind energy assessment (WEA) in China. Four bandwidth selectors, including normal scale (NS), plug in, biased cross-validation, and least-square cross validation, are proposed for the KDE model. Popular parametr...

在风力发电场的合理选址以及风力发电机组的结构设计阶段，能量谱与功率谱模型对于风力发电机组的 系统方案有着直接的影响，特别是在具体结构设计阶段选取恰当的功率谱模型是至关重要的，但目前对具体模型的选择 依据尚不明确。针对能量谱与功率谱两种重要的风速数据分析模型，以国内五处地区代表性风场的全年风速测试数据为 基础，进行了能量谱与功率谱的分析，从分析结果中得出了如下两个结论: ① 我国东南沿海地区风场的风力资源最为丰 富，其有效风能密度区( 200 ～ 400 W/m2 ) 持续时间最长; 北部地区风场的风力资源也较为丰富，且在高风能密度区( ＞ 500 W/m2 ) 具有一定优势; 对于主要风力资源集中在低风能密度区( ＜ 200 W/m2 ) 的部分地区风场，在风力发电设备选型时应 该有针对性的...

Feature extraction has always been a significant research topic for in-situ fault diagnosis applications. In this research, measurement uncertainty of vibration signal is defined and extracted as a pre-processing step for statistical feature calculation. An Empirical Mode Decomposition (EMD) detrending method combined with hurst exponent criterion...

Rotor structures in abnormal states will create non-stationary vibration sources, these non-stationary features are often ignored by traditional signal processing algorithms, thereby the determination of rotor running states is affected. A new state specificity perception method based on kernel density function estimation combined with higher momen...

Novelty detection has been developed into a state-of-the-art technique to detect abnormal behavior and trigger alarm for in-field machine maintenance. With built-up models of normality, it has been widely applied to several situations with normal supervising dataset such as shaft rotating speed and component temperature available meanwhile in the a...

为综合分析叶片振动特征，以基础类库MFC 为框架构建多种有限元软件( ANSYS 及PATRAN) 的融合平 台，并开发相应的控制模块实现完整的流程控制。由所设计平台进行并行计算，获得压气机叶片的固有特性及其 在气动激励诱导下的受迫振动特性。并行方式高效可行，且计算结果特征明显，表明平台具有良好的工程应用价值。