An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers [PDF]
Sensors, 2022 In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults ...
Qitong Xu +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.
Chuanhua Deng +4 more
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Compound mechanical fault diagnosis based on CMDE [PDF]
Advances in Mechanical Engineering, 2022 The fault diagnosis technique is of important for the safety operation of the rotating machinery. In the fault diagnosis framework, the entropy-based method is a promising tool for the feature extraction and signal processing.
Guangwei Yu, Xianzhi Wang, Chunlin Da
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Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis [PDF]
Sensors, 2023 Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems.
Qinghong Xu +4 more
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Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings [PDF]
Heliyon, 2023 Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data.
Liuxing Chu +5 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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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 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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A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis [PDF]
Sensors, 2023 This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals.
Shuzhen Han +6 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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