The hydraulic system is crucial for the safety of the aircraft, which is the key to ensuring the safety of both the aircraft and passengers. It is necessary to study and analyze the normal and fault mode of the system to provide a way for evaluating the fault degree of the hydraulic system. Therefore an improved ResNet based fault degree evaluation method was proposed to evaluate the fault degree of the aircraft hydraulic system. First, the aircraft hydraulic system is constructed by the AMESim, one normal and five fault modes are simulated. Then the effects of the parametric variations for the five fault models are studied, in which pump oil leakage is chosen for analysis. After the analysis of the 5 kinds of faults, each of them is divided to 3 different fault degree, then 16 kinds of states are definition. Second, using the SE-ResNet based method to evaluate the system fault degree. The structure of the two improved ResNet blocks are designed, after that the whole structure of the SE-ResNet fault degree evaluation model is given. Then the parameters of the SE-ResNet are optimization by simulation. After that the evaluate results are given and analyzed, moreover the comparison between the SE-ResNet method with the other machine learning methods are given. The results show that the method in this paper has the best accuracy and shortest test time, therefore the method proposed in this paper has effective measures to improve the reliability of the aircraft hydraulic system.
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http://dx.doi.org/10.1038/s41598-025-86634-3 | DOI Listing |
Heliyon
February 2025
Department of Electrical and Electronic Engineering, Meru University of Science and Technology, P.O BOX, 972-60200, Meru, Kenya.
A substantial number of power transformers that are in use mainly in developing countries are aged and operating beyond their technical design life. This has forced many power utility entities to embrace condition-based maintenance strategies in an effort to prolong assets functionality and reduce equipment failures. To maximize the continuous use of aging power system assets, it is essential to comprehend the variables that pose a threat to the technical and operational lifetime.
View Article and Find Full Text PDFNature
February 2025
AWS Center for Quantum Computing, Pasadena, CA, USA.
To solve problems of practical importance, quantum computers probably need to incorporate quantum error correction, in which a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead associated with error correction motivates the search for more hardware-efficient approaches. Here, using a superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance d = 5 (ref.
View Article and Find Full Text PDFJ Adv Res
February 2025
National Key Lab of Aerospace Power System and Plasma Technology, Xi'an Jiaotong University, Xi'an 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Institute for Infrastructure and Environment, School of Engineering, The University of Edinburgh, Edinburgh EH8 8AQ, UK.
Accurately predicting the remaining useful life (RUL) of complex equipment plays a vital role in maintaining modern manufacturing systems' operational safety and reliability. This challenge has attracted considerable interest within the domain of intelligent operation and maintenance. However, the lack of high-quality, comprehensive lifecycle data in industrial environments is a major barrier to developing and deploying intelligent RUL prediction algorithms.
View Article and Find Full Text PDFSensors (Basel)
February 2025
School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China.
In the process of diagnosing the inter-turn short circuit fault of the joint permanent magnet synchronous motor of an industrial robot, due to the small and sparse fault sample data, it is easy to misdiagnose, and it is difficult to quickly and accurately evaluate the fault degree, lock the fault location, and track the fault causes. A multi-task causal knowledge fault diagnosis method for inter-turn short circuits of permanent magnet synchronous motors based on meta-learning is proposed. Firstly, the variation of parameters under the motor's inter-turn short circuit fault is thoroughly investigated, and the fault characteristic quantity is selected.
View Article and Find Full Text PDFEntropy (Basel)
February 2025
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
In the health monitoring of electromechanical transmission systems, the collected state data typically consist of only a minimal amount of labeled data, with a vast majority remaining unlabeled. Consequently, deep learning-based diagnostic models encounter the challenge of scarcity in labeled data and abundance in unlabeled data. Traditional semi-supervised deep learning methods based on pseudo-label self-training, while alleviating the issue of labeled data scarcity to some extent, neglect the reliability of pseudo-label information, the accuracy of feature extraction from unlabeled data, and the imbalance in sample selection.
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