Vibration signal-based early fault prognosis: Status quo and applications
Abstract To implement Prognostics and Health Management (PHM) for industrial systems, it
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning
D He, C Liu, Z Jin, R Ma, Y Chen, S Shan - Energy, 2022 - Elsevier
Flywheel energy storage system is widely used in train braking energy recovery, and has
achieved excellent energy-saving effect. As a key component of the flywheel energy storage …
achieved excellent energy-saving effect. As a key component of the flywheel energy storage …
Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder
The collected vibration data with labeled information from bearing is far insufficient in
engineering practice, which is challenging for training an intelligent diagnosis model. For …
engineering practice, which is challenging for training an intelligent diagnosis model. For …
Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising
C Yin, Y Wang, G Ma, Y Wang, Y Sun, Y He - Mechanical Systems and …, 2022 - Elsevier
Extracting weak fault features under noise interference is crucial for the fault diagnosis of
rolling bearings at an early stage. In this paper, a new method based on improved ensemble …
rolling bearings at an early stage. In this paper, a new method based on improved ensemble …
Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image
Y Sun, S Li, X Wang - Measurement, 2021 - Elsevier
A novel bearing fault diagnosis on basis of empirical mode decomposition (EMD) and
improved Chebyshev distance is presented. After normalization, each group of sample data …
improved Chebyshev distance is presented. After normalization, each group of sample data …
Fault detection of the harmonic reducer based on CNN-LSTM with a novel denoising algorithm
The harmonic reducer is a key component of the industrial robot. Its reliability has significant
influence on the consecutive operation of the industrial robot. However, its failure rate is high …
influence on the consecutive operation of the industrial robot. However, its failure rate is high …
Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method
A precise wind power forecast is required for the renewable energy platform to function
effectively. By having a precise wind power forecast, the power system can better manage its …
effectively. By having a precise wind power forecast, the power system can better manage its …
Compound fault diagnosis for industrial robots based on dual-transformer networks
The accurate diagnosis of the compound fault of industrial robots can be highly beneficial to
maintenance management. In the actual noisy working environment of industrial robots, the …
maintenance management. In the actual noisy working environment of industrial robots, the …
[HTML][HTML] Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review
A Anwarsha, T Narendiranath Babu - Journal of Vibroengineering, 2022 - extrica.com
A rolling element bearing is a common component in household and industrial machines.
Even a minor fault in this section has a negative impact on the machinery's overall operation …
Even a minor fault in this section has a negative impact on the machinery's overall operation …
Vibration signal denoising for structural health monitoring by residual convolutional neural networks
In vibration based structural health monitoring (SHM), measurement noise inevitably exists
in the vibration data, which significantly influences the usability and quality of measured …
in the vibration data, which significantly influences the usability and quality of measured …