Biprobes Blade Tip Timing Method for Frequency Identification Based on Active Aliasing Time-Delay Estimation and Dealiasing

Cao, Jiahui; Yang, Zhibo; Tian, Shu; Li, Haoqi; Jin, Ruocheng; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/tie.2022.3165252

Cited by 22 publications

(2 citation statements)

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“…Differences in the waveforms in the TD, FD, and TFD are shown through the signal comparison in Section 2.1 . Statistical feature calculation in the TD [ 40 , 41 ], FD [ 42 ], and TFD [ 43 , 44 ] is conducted, and 40 features are constructed to capture the fault characteristics from different aspects.…”

Section: Methodsmentioning

confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

Materials

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A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

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Section: Methodsmentioning

confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

Materials

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“…Ultrasound-Guided Wired Magnetic Microrobot With Active Steering and Ejectable Tip; TIE Jan. 2023 614-623 + Check author entry for coauthors Yang, Z., see Sun, W., TIE Jan. 2023 699-708 Yang, Z., see Cao, J.,1939-1948 …”

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confidence: 99%

2023 Index IEEE Transactions on Industrial Electronics Vol. 70

2023

IEEE Trans. Ind. Electron.

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This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2023, and items from previous years that were commented upon or corrected in 2023. Departments and other items may also be covered if they have been judged to have archival value.The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

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Single-sensor rotating blade monitoring method under non-stationary conditions based on velocity and displacement

Bian,

Yang,

Guan

et al. 2024

ISA Transactions

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Biprobes Blade Tip Timing Method for Frequency Identification Based on Active Aliasing Time-Delay Estimation and Dealiasing

Cited by 22 publications

References 18 publications

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

2023 Index IEEE Transactions on Industrial Electronics Vol. 70

Single-sensor rotating blade monitoring method under non-stationary conditions based on velocity and displacement

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