Improved Empirical Wavelet Transform for Compound Weak Bearing Fault Diagnosis with Acoustic Signals
Applied Sciences, 2020 Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases.
Chaoren Qin +3 more
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Intelligent Compound Fault Diagnosis of Roller Bearings Based on Deep Graph Convolutional Network. [PDF]
Sensors (Basel), 2023 The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network.
Chen C, Yuan Y, Zhao F.
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Application of improved FastICA algorithm in compound fault diagnosis of vibrating scree
Gong-kuang zidonghua, 2017 In view of problem that common fault characteristic extraction methods could not extract compound fault characteristic completely and effectively, an improved fast independent component analysis(FastICA) algorithm was proposed.
XU Yuanbo, CAI Zongyan
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A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV
Applied Sciences, 2021 In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment.
Ke Zheng +3 more
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Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties [PDF]
Actuators, 2020 This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the ...
Ming Yu +4 more
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Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks
SensorsGearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis.
Jianhui Cao +4 more
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Shock and VibrationThe coupling torque signal contains essential information about the operating condition of the motor-follower mechanical system. Artificial Intelligence methods have been effective in diagnosing coupling faults.
Fu Liu, Haopeng Chen, Yan Wang
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Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network. [PDF]
Sensors (Basel), 2020 Gear transmission is widely used in mechanical equipment. In practice, if the gearbox is damaged, it not only affects the yield rate but also damages other parts of machines; thus, increases the cost and difficulty of maintenance. With the advancement of
Lin MC, Han PY, Fan YH, Li CG.
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Compound Fault Diagnosis for Gearbox based on Variational ModeDecomposition and Correlated Kurtosis
Jixie chuandong, 2019 Aiming at the characteristics of non-linear, non-stationary, multi-noise and multi-fault signals of the compound fault signal in gearboxes, a compound fault diagnosis method for gearboxes based on variational mode decomposition and correlated kurtosis is
Zhang Xin +4 more
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Bearing Fault Diagnosis Based on Multilayer Domain Adaptation
Shock and Vibration, 2020 Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which
Bingru Yang +3 more
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