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A Novel Method for Actuator Degradation Assessment Based on Improved Multifractal Analysis
Sun Tianshu,Zheng Lin,Liu Jin,Wang Yinsong,Zhang Yu 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
In the process industry, actuators regulate the flow of process masses in response to instructions from a controller, which achieves the control objectives of the system. However, many factors cause the performance of the actuator to degrade. Therefore, it is of great significance to evaluate the actuator operating performance correctly for equipment maintenance and system optimization. In response, this paper designs an improved multifractal method based on statistical moment functions. And a set of indicators-based degradation assessment system is constructed. First, the time series collected from the actuator is divided into two different density zones, where evaluation indicators are established respectively to qualitatively describe the performance from different perspectives. Then, a degradation index calculated by weighted fusion is applied to quantitatively evaluate the dynamic degradation state of the actuator. Finally, the effectiveness and practicability of the method are experimentally verified through two field cases in thermal power units.
Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions
Wang Yinsong,Sun Tianshu,Liu Yanyan 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.6
The gearbox is an important part of the wind turbine, and it is also a part prone to failure. Most of the fault diagnosis methods for gearboxes lack the ability to adapt to changing operating conditions. Once the operating conditions change, it is necessary to relearn the object characteristics, otherwise it is prone to misjudgment. Therefore, this paper proposes a method based on a two-class non-negative matrix factorization (NMF) network to realize gearbox fault diagnosis under variable operating conditions without additional data. The method is divided into two parts: model training and fault diagnosis. The former takes the basic operating conditions as the benchmark. It extracts the local static characteristics of the fault samples, and trains them into classifi er models with diff erent diagnostic functions to construct a network. The latter uses the static distance obtained from the data input network of the new operating condition as an important indicator for detecting the occurrence of a fault and identifying the type of the fault. The experimental results based on the QPZZ-II rotating machinery vibration test stand show that compared with other methods, the algorithm proposed in this paper has a good application eff ect on the diagnosis under variable operating conditions
KMT-2016-BLG-1397b: KMTNET-only Discovery of a Microlens Giant Planet
Zang, Weicheng,Hwang, Kyu-Ha,Kim, Hyoun-Woo,Gould, Andrew,Wang, Tianshu,Zhu, Wei,Mao, Shude,Albrow, Michael D.,Chung, Sun-Ju,Han, Cheongho,Jung, Youn Kil,Ryu, Yoon-Hyun,Shin, In-Gu,Shvartzvald, Yossi American Astronomical Society 2018 The Astronomical journal Vol.156 No.5