Results 231 to 240 of about 31,094 (285)

Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis

IEEE Transactions on Instrumentation and Measurement, 2020
The vibration monitoring of gearboxes is an effective means of ensuring the long-term safe operation of rotating machinery. A gearbox may have more than one fault in actual applications. Therefore, gearbox compound fault diagnosis should be investigated.
Ning Li, Weiguo Huang, Wenjun Guo, Guanqi Gao, Zhongkui Zhu   +4 more
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A Novel Compound Neural Network for Fault Diagnosis

2006 2nd IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, 2006
Independent Component Analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, Artificial Neural Network (ANN), especially the Self-Organizing Map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, we proposed a novel compound neural
Jiao Weidong, Yang Shixi, Yan Gongbiao
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Compound fault diagnosis based on probability box theory

2017 9th International Conference on Modelling, Identification and Control (ICMIC), 2017
In view of compound fault of rolling bearing in mechanical transmission system, the fault signal was decomposed by probability box modeling method to obtain the different probability box models. The original data with abundant statistical information was taken as the research object.
Tang Hong, Du Yi, Ding Jiaman, Liu Liqiang   +3 more
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A novel compound fault diagnosis method using intrinsic component filtering

Measurement Science and Technology, 2020
Abstract: Compound fault diagnosis of gearbox, which can prevent breakdown accidents and minimize production loss, has become a hotspot and challenge. In practical application, the early fault signal is weak and often concealed by environmental noise. Therefore, how to to separate different fault components from the signals with weak faults and strong ...
Zongzhen Zhang, Shunming Li, Yu Xin, Huijie Ma   +3 more
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Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method

IEEE Transactions on Control Systems Technology, 2021
Rotating machinery plays an important role in large-scale equipment. The fault diagnosis of rotating machinery is of great significance and can increase industrial safety. Up until now, most existing fault diagnosis techniques have been proposed under the condition that only a single fault will occur at the same time.
Jingfei Zhang, Qinghua Zhang, Xiao He, Guoxi Sun, Donghua Zhou   +4 more
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Compound fault diagnosis of gearboxes based on GFT component extraction

Measurement Science and Technology, 2016
Compound fault diagnosis of gearboxes is of great importance to the long-term safe operation of rotating machines, and the key is to separate different fault components. In this paper, the path graph is introduced into the vibration signal analysis and the graph Fourier transform (GFT) of vibration signals are investigated from the graph spectrum ...
Lu Ou, Dejie Yu
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A New ICA-SOM Based Method for Compound Faults Diagnosis

2006 6th World Congress on Intelligent Control and Automation, 2006
Compound faults diagnosis is an important but difficult task in diagnostics. When several faults arise at the same time, vibration measurements by sensors show themselves as a complex integrated symptom, not as a simple and linear combination of several single faults, which makes it difficult to detect compound faults correctly.
null Weidong Jiao, null Shixi Yang, null Suxiang Qian, null Gongbiao Yan   +3 more
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A zero-shot fault semantics learning model for compound fault diagnosis

Expert Systems with Applications, 2023
Juan Xu, Shaokang Liang, Xu Ding, Ruqiang Yan   +3 more
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Adversarial weighted multi-source domain adaptation for compound fault diagnosis

Measurement Science and Technology
Abstract Diagnosing compound faults in rotating machinery remains a major challenge due to the scarcity and imbalance of labeled training data, especially under varying operating conditions. To address this issue, this paper proposes a multi-source adversarial domain adaptation (MSADA) framework that integrates data augmentation, dynamic
Wenjing Zhou, Liuxing Chu, Qitong Chen, Changqing Shen, Liang Chen   +4 more
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Generative Zero-shot Learning Compound Fault Diagnosis of Bearings

2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), 2021
Juan Xu, Kang Li
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