Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network [PDF]
SensorsWind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein,
Weixiong Jiang +4 more
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Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing [PDF]
Entropy, 2019 The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods
Yonggang Xu +4 more
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Compound Fault Feature Extraction of Rolling Bearing Acoustic Signals Based on AVMD-IMVO-MCKD [PDF]
Sensors, 2022 The compound fault acoustic signal of a rolling bearing has the characteristics of a varying noise mixture, a low signal-to-noise ratio (SNR), and nonlinearity, which makes it difficult to separate and extract exactly the fault features of compound fault
Shishuai Wu, Jun Zhou, Tao Liu
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Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network [PDF]
Sensors, 2023 The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context.
Xiaoyang Zheng +5 more
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Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network [PDF]
SensorsThe identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient ...
Guoyan Li +5 more
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Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition [PDF]
Sensors, 2022 Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults
Yang Feng +3 more
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Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors [PDF]
Sensors, 2023 Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important
Taehwan Son, Dongwoo Hong, Byeongil Kim
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Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation [PDF]
Entropy, 2020 Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and ...
Guiji Tang, Tian Tian
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A New Compound Fault Feature Extraction Method Based on Multipoint Kurtosis and Variational Mode Decomposition [PDF]
Entropy, 2018 Due to the weak entropy of the vibration signal in the strong noise environment, it is very difficult to extract compound fault features. EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition) and LMD (Local Mean Decomposition ...
Wenan Cai +3 more
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Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots [PDF]
SensorsThe increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper
Chuanhua Deng +4 more
openaire +4 more sources